A Beidou monitoring method for high slope along the layer

Abstract

The present invention relates to the field of geological engineering, and discloses a Beidou monitoring method for high slopes along the layers, the method comprising: analyzing the topographic map and geomorphic features of the high slopes along the layers, determining the monitoring points and reference points of the high slopes along the layers, and constructing a Beidou monitoring module for the monitoring points; calculating the monitoring reliability coefficient of the Beidou monitoring module, monitoring the slope deformation data of the monitoring points in real time, and constructing a deformation analysis model for the high slopes along the layers; calculating the anti-sliding force and sliding force of the high slopes along the layers, and constructing a slope state model for the high slopes along the layers; constructing a slope state influence model for the high slopes along the layers, coupling the slope state influence model, the slope state model, and the deformation analysis model, analyzing the displacement-time analysis curve of the high slopes along the layers using the slope displacement analysis model, calculating the displacement anomaly coefficient of the high slopes along the layers, constructing an early warning mechanism for the high slopes along the layers, and executing intelligent monitoring and early warning for the high slopes along the layers. The present invention can improve the monitoring efficiency of the high slopes along the layers.

Description

Technical Field

[0001] The invention relates to the field of geological engineering, and in particular to a Beidou monitoring method for high slopes along a layer. Background Art

[0002] A high slope along the layer is a slope whose inclination direction is consistent with the inclination of the stratum or rock layer. For this type of slope, the layer of the rock layer is usually parallel or nearly parallel to the inclined surface of the slope. Due to the stratified structure of the rock layer or stratum, the high slope along the layer may slide along the layer or other forms of instability under the influence of geological structure, changes in hydrological conditions or human activities (such as excavation).

[0003] Existing soil horizontal displacement, such as the horizontal displacement of foundation pits or pile holes, is measured by using an inclinometer with an internal track that is pre-buried or drilled and installed later. The existing method requires the inclinometer in the inclinometer to be manually pulled to measure an inclination angle per meter along the depth direction, and then the horizontal displacement at the depth position is reversed according to the inclination angle, and a horizontal displacement curve that changes with depth is drawn, which is soil inclinometer. However, this method is laborious and inefficient. Summary of the invention

[0004] The present invention provides a Beidou monitoring method for high slopes along the layers, the main purpose of which is to improve the monitoring efficiency of high slopes along the layers.

[0005] To achieve the above purpose, the present invention provides a Beidou monitoring method for high slopes along the layer, comprising:

[0006] Analyze the topographic map and geomorphic features of the high slope along the layer, determine the monitoring points and reference points of the high slope along the layer based on the topographic map and geomorphic features, and construct a Beidou monitoring module for the monitoring points;

[0007] Calculating the monitoring reliability coefficient of the Beidou monitoring module, and when the monitoring reliability coefficient meets the preset reliability coefficient, using the Beidou monitoring module and the reference point to monitor the slope deformation data of the monitoring point in real time, and constructing the deformation analysis model of the layer-by-layer high slope based on the slope deformation data;

[0008] Obtaining slope parameters of the high slope along the layer, calculating the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters, and constructing a slope state model of the high slope along the layer based on the anti-sliding force, the sliding force and the slope parameters;

[0009] Based on the slope state model, determine the slope state of the high slope along the layer, identify the influencing factors that affect the slope state, construct a relationship curve between the slope state and the influencing factors, and based on the relationship curve, construct a slope state influence model of the high slope along the layer, and couple the slope state influence model, the slope state model and the deformation analysis model to obtain a slope displacement analysis model;

[0010] Based on the slope deformation data, the displacement-time analysis curve of the high slope along the layer is analyzed using the slope displacement analysis model. Based on the displacement-time analysis curve, the displacement anomaly coefficient of the high slope along the layer is calculated. According to the displacement anomaly coefficient, an early warning mechanism for the high slope along the layer is constructed. Based on the early warning mechanism, intelligent monitoring and early warning of the high slope along the layer is performed.

[0011] Optionally, determining the monitoring points and reference points of the high slope along the layer based on the topographic map and the landform features includes:

[0012] Based on the topographic map and geomorphic features, a geological survey is conducted on the high slope along the layer to obtain the geological structure of the slope;

[0013] Analyze the potential landslide risk area and relatively stable area of ​​the high slope along the layer based on the geological structure of the slope;

[0014] Determining monitoring points of the high slope along the layer according to the potential landslide risk area;

[0015] According to the relatively stable area, the reference point of the layer-adjacent high slope is determined.

[0016] Optionally, the Beidou monitoring module for constructing the monitoring point includes:

[0017] Determine the number of monitoring points and the distribution of monitoring points of the monitoring points;

[0018] According to the number and distribution of the monitoring points, configure Beidou receivers and sensors at the monitoring points;

[0019] Constructing a data acquisition unit and a communication module for the monitoring point;

[0020] Integrate the Beidou receiver, sensor, data acquisition unit and communication module into a monitoring point module;

[0021] Analyzing the location environment of the monitoring point, and configuring the support rod of the monitoring point module based on the location environment;

[0022] Constructing a fixing unit of the support rod according to the geomorphic features corresponding to the monitoring point;

[0023] The Beidou monitoring module of the monitoring point is constructed by means of the monitoring point module, the support rod and the fixing unit.

[0024] Optionally, the calculating the monitoring reliability coefficient of the Beidou monitoring module includes:

[0025] Testing the Beidou monitoring module to obtain multiple groups of test data;

[0026] According to the multiple groups of test data, the Beidou monitoring module collects multiple groups of actual data of the same number of monitoring points;

[0027] The monitoring reliability coefficient of the Beidou monitoring module is calculated based on the multiple groups of test data and the multiple groups of actual data.

[0028] Optionally, the calculating the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters includes:

[0029] Based on the slope parameters, determining the sliding body and sliding surface of the high slope along the layer;

[0030] Dividing the sliding body according to a preset dividing angle to obtain sliding body strips;

[0031] Analyze the stress state of the sliding body strip, determine the vertical angle of the sliding surface, and analyze the internal friction angle of the sliding body strip;

[0032] Calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle;

[0033] The anti-sliding force and the sliding force of the high slope along the layer are calculated according to the thrust coefficient of the sliding body, the stress state, the vertical angle and the internal friction angle.

[0034] Optionally, the calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle includes:

[0035] Obtaining the slider length of the slider;

[0036] The sliding body thrust coefficient of the sliding body block is calculated according to the vertical angle, the internal friction angle and the length of the sliding body block.

[0037] Optionally, constructing the slope state influence model of the layer-by-layer high slope based on the relationship curve includes:

[0038] Acquire multiple groups of relationship data of the relationship curve;

[0039] Based on the relationship curve, a linear regression model of the layer high slope is constructed;

[0040] Calculating the slope parameter and intercept parameter of the linear regression model according to the multiple sets of relationship data;

[0041] The linear regression model is updated by using the model parameters to obtain the slope state influence model of the layer-by-layer high slope.

[0042] Optionally, coupling the slope state influence model, the slope state model and the deformation analysis model to obtain the slope displacement analysis model includes:

[0043] Determining a common data format of the slope state influence model, the slope state model, and the deformation analysis model;

[0044] Based on the data format, a data exchange mechanism for the slope state influence model, the slope state model and the deformation analysis model is constructed;

[0045] Constructing a model integration architecture for managing data flow between the slope state impact model, the slope state model, and the deformation analysis model;

[0046] According to the model integration architecture, a coupling mechanism of the slope state influence model, the slope state model and the deformation analysis model is constructed;

[0047] The slope state influence model, the slope state model and the deformation analysis model are coupled through the data exchange mechanism, the model integration architecture and the coupling mechanism to obtain an initial coupling model;

[0048] Obtaining model prediction values ​​and actual observation values ​​of the initial coupling model;

[0049] Calculating a mean square error of the initial coupling model based on the model prediction value and the actual observation value;

[0050] When the average error meets a preset error threshold, the initial coupling model is used as a slope displacement analysis model.

[0051] Optionally, the calculating the displacement anomaly coefficient of the layer-by-layer high slope based on the displacement-time analysis curve includes:

[0052] Determining the reference displacement of the layer-by-layer high slope;

[0053] Calculating the mean and standard deviation of the reference displacement;

[0054] Based on the displacement-time analysis curve, obtaining the displacement values ​​of the layered high slope at different time points;

[0055] The displacement anomaly coefficient of the high slope along the layer is calculated according to the average value and standard deviation of the reference displacement and the displacement value of the high slope along the layer.

[0056] Optionally, constructing a relationship curve between the slope state and the influencing factor includes:

[0057] Acquiring multi-source data of the slope status and influencing factors;

[0058] Preprocessing the multi-source data to obtain preprocessed multi-source data;

[0059] Constructing a scatter plot of the relationship between the slope state and the influencing factors according to the preprocessed multi-source data;

[0060] Analyzing the distribution of scatter point coordinates of the relationship scatter plot;

[0061] Based on the scatter point coordinate distribution, the relationship scatter point diagram is fitted into a relationship curve.

[0062] The embodiment of the present invention can more effectively monitor the displacement and stress changes of the slope by determining the monitoring points and reference points of the high slope along the layer based on the topographic map and landform features; optionally, the embodiment of the present invention can realize real-time, continuous and high-precision monitoring of the high slope along the layer by constructing the Beidou monitoring module of the monitoring point; the embodiment of the present invention can quantitatively evaluate the performance of the monitoring system by calculating the monitoring reliability coefficient of the Beidou monitoring module, thereby ensuring the quality of monitoring data and the reliability of the system; the embodiment of the present invention can evaluate the risk level of the high slope along the layer by calculating the anti-sliding force and sliding force of the high slope along the layer based on the slope parameters; the embodiment of the present invention can make the analysis of the influencing factors of the slope stability more accurate and specific by constructing the relationship curve between the slope state and the influencing factors, and finally, the embodiment of the present invention can identify the abnormal situation of the slope displacement by calculating the displacement anomaly coefficient of the high slope along the layer based on the displacement-time analysis curve, thereby issuing an early warning in time. Therefore, the present invention can improve the monitoring efficiency of the high slope along the layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a process flow of a Beidou monitoring method for high slopes along a layer provided by an embodiment of the present invention;

[0064] Figure 2 A functional module diagram of a Beidou monitoring system for high slopes along a layer provided by an embodiment of the present invention;

[0065] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0067] The embodiment of the present application provides a Beidou monitoring method for high slopes along the layers. The execution subject of the Beidou monitoring method for high slopes along the layers includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the Beidou monitoring method for high slopes along the layers can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0068] Reference Figure 1 FIG. 1 is a flow chart of a Beidou monitoring method for high slopes along a layer provided by an embodiment of the present invention. In this embodiment, the Beidou monitoring method for high slopes along a layer includes:

[0069] S1. Analyze the topographic map and geomorphic features of the high slope along the layer, determine the monitoring points and reference points of the high slope along the layer based on the topographic map and geomorphic features, and construct a Beidou monitoring module for the monitoring points.

[0070] The embodiment of the present invention can identify potential risk areas of the slope by analyzing the topographic map and geomorphic features of the high slope along the layer, and more effectively manage and maintain the high slope along the layer. The topographic map refers to a map that shows the surface morphology and features in detail. The geomorphic features refer to the natural morphology and surface features of the surface of the high slope along the layer, which are formed by natural forces (such as water, wind, ice, geological activities, etc.) acting on the surface over a long period of time.

[0071] Optionally, as an embodiment of the present invention, the topographic map and geomorphic features of the layered high slope can be analyzed by geographic information system technology and remote sensing technology, wherein the geographic information system technology refers to a system for capturing, storing, managing, analyzing and displaying spatial or geographic information. The remote sensing technology refers to a method of collecting information on the earth's surface features from an aircraft, satellite or other remote platform using sensors.

[0072] The embodiment of the present invention can more effectively monitor the displacement and stress changes of the slope by determining the monitoring points and reference points of the layer-by-layer high slope based on the topographic map and geomorphic features. The monitoring points refer to fixed positions arranged on the slope for monitoring parameters such as slope displacement, stress, and temperature. The reference points refer to one or more fixed positions set in the monitoring area, which are used to provide an accurate reference coordinate system to ensure the accuracy and consistency of all monitoring data.

[0073] As an embodiment of the present invention, the determining of the monitoring points and reference points of the high slope along the layer based on the topographic map and the landform features includes:

[0074] Based on the topographic map and geomorphic features, a geological survey is conducted on the high slope along the layer to obtain the geological structure of the slope;

[0075] Analyze the potential landslide risk area and relatively stable area of ​​the high slope along the layer based on the geological structure of the slope;

[0076] Determining monitoring points of the high slope along the layer according to the potential landslide risk area;

[0077] According to the relatively stable area, the reference point of the layer-adjacent high slope is determined.

[0078] The slope geological structure refers to the geological composition of the high slope along the layer, including stratum lithology, faults, joints, fissures, etc. The potential landslide risk area refers to the area where landslides may occur based on the analysis of the slope geological structure. The relatively stable area refers to the area where landslides are relatively unlikely to occur based on the analysis of the slope geological structure.

[0079] The embodiment of the present invention can realize real-time, continuous and high-precision monitoring of the high slope along the layer by constructing the Beidou monitoring module of the monitoring point. The Beidou monitoring module refers to a modular device that uses the high-precision positioning function of the Beidou satellite navigation system (BDS) in combination with other sensors and communication technologies to realize the monitoring and management of the specific target position and status.

[0080] As an embodiment of the present invention, the Beidou monitoring module for constructing the monitoring point includes:

[0081] Determine the number of monitoring points and the distribution of monitoring points of the monitoring points;

[0082] According to the number and distribution of the monitoring points, configure Beidou receivers and sensors at the monitoring points;

[0083] Constructing a data acquisition unit and a communication module for the monitoring point;

[0084] Integrate the Beidou receiver, sensor, data acquisition unit and communication module into a monitoring point module;

[0085] Analyzing the location environment of the monitoring point, and configuring the support rod of the monitoring point module based on the location environment;

[0086] Constructing a fixing unit of the support rod according to the geomorphic features corresponding to the monitoring point;

[0087] The Beidou monitoring module of the monitoring point is constructed by means of the monitoring point module, the support rod and the fixing unit.

[0088] Among them, the number of monitoring points and the distribution of monitoring points refer to the number of monitoring points that need to be set and the specific location distribution of these monitoring points on the slope in the layer-by-layer high slope monitoring project, according to project requirements, geological characteristics of the slope, potential risk areas, and monitoring purposes. The Beidou receiver is used to receive Beidou satellite signals and provide high-precision location information. The sensor is selected according to monitoring requirements, such as displacement sensors, stress sensors, temperature sensors, etc., for monitoring parameters such as displacement, stress, and temperature of the slope. The data acquisition unit is responsible for collecting data from the Beidou receiver and sensor and performing preliminary processing. The communication module is used to transmit the collected data to the monitoring center, which can be wireless or wired communication. The monitoring point module refers to a modular device that integrates a Beidou receiver, a sensor, a data acquisition unit, and a communication module. The location environment refers to the specific environment where the monitoring point is located, including terrain, landform, climate and other conditions. The support rod refers to a device used to fix and support the monitoring point module to ensure its stability and data accuracy. The fixing unit refers to a device used to firmly fix the support rod on the slope to resist the influence of factors such as wind and vibration.

[0089] Optionally, the data acquisition unit and communication module for constructing the monitoring point can realize the collection, processing, storage and transmission logic of sensor data and the construction of communication protocol with a remote monitoring center by developing the software of the data acquisition unit and the software of the communication module using a programming language (such as C, Python, etc.).

[0090] S2. Calculate the monitoring reliability coefficient of the Beidou monitoring module. When the monitoring reliability coefficient meets the preset reliability coefficient, use the Beidou monitoring module and the reference point to monitor the slope deformation data of the monitoring point in real time, and construct a deformation analysis model of the layer-by-layer high slope based on the slope deformation data.

[0091] The embodiment of the present invention can quantitatively evaluate the performance of the monitoring system by calculating the monitoring reliability coefficient of the Beidou monitoring module, thereby ensuring the quality of the monitoring data and the reliability of the system. The monitoring reliability coefficient refers to the degree of closeness between the monitoring data and the actual value, which reflects the reliability and accuracy of the monitoring system.

[0092] As an embodiment of the present invention, the calculating of the monitoring reliability coefficient of the Beidou monitoring module includes:

[0093] Testing the Beidou monitoring module to obtain multiple groups of test data;

[0094] According to the multiple groups of test data, the Beidou monitoring module collects multiple groups of actual data of the same number of monitoring points;

[0095] According to the multiple groups of test data and multiple groups of actual data, the monitoring reliability coefficient of the Beidou monitoring module is calculated using the following formula:

[0096]

[0097] Among them, K b represents the monitoring reliability coefficient, N represents the number of test data sets, S i Indicates the corresponding i-th group of data in multiple groups of actual data, C i The corresponding i-th group of data in multiple groups of test data.

[0098] The multiple groups of test data refer to a series of data obtained by testing the Beidou monitoring module. The multiple groups of actual data refer to a series of data collected from monitoring points in a real environment, and the number of groups of the multiple groups of actual data is the same as that of the multiple groups of test data.

[0099] Optionally, the testing of the Beidou monitoring module to obtain multiple groups of test data can be performed by simulating various application environments of the Beidou monitoring module.

[0100] The embodiment of the present invention can update the slope stability information in real time by using the Beidou monitoring module and the reference point to monitor the slope deformation data of the monitoring point in real time when the monitoring reliability coefficient meets the preset reliability coefficient, so as to timely discover the potential landslide or collapse risk. The slope deformation data refers to the data of the deformation of the slope in space and time, such as displacement data, stress data and strain data.

[0101] The embodiment of the present invention can better understand and predict the stability of the slope by constructing the deformation analysis model of the layered high slope based on the slope deformation data, so as to take corresponding measures to ensure the safety of the slope. The deformation analysis model is a mathematical model used to simulate and analyze the deformation process of the slope in time and space.

[0102] Optionally, the deformation analysis model of the high slope along the layer based on the slope deformation data can be constructed by numerical simulation technology, wherein the numerical simulation technology refers to a method of simulating the deformation process of complex high slope along the layer in the real world by numerical calculation.

[0103] S3. Obtain slope parameters of the high slope along the layer, calculate the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters, and construct a slope state model of the high slope along the layer based on the anti-sliding force, the sliding force and the slope parameters.

[0104] The embodiment of the present invention can be helpful in determining the potential instability mode of the slope by obtaining the slope parameters of the high slope in the layer. The slope parameters refer to various indicators and data used to describe the geological characteristics and physical and mechanical properties of the slope, such as rock and soil type, rock and soil structure, and physical and mechanical properties.

[0105] Optionally, the slope parameters of the layered high slope may be obtained by geophysical exploration methods, wherein the geophysical exploration refers to the use of geophysical methods such as seismic waves, electrical methods, magnetic methods, etc. to detect the rock properties, structure, cracks, etc. inside the slope.

[0106] The embodiment of the present invention can evaluate the risk level of the high slope along the layer by calculating the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters. The anti-sliding force refers to the force that prevents the slope rock and soil from sliding along the potential sliding surface in the high slope along the layer. The sliding force refers to the force that causes the slope rock and soil to slide along the potential sliding surface in the high slope along the layer.

[0107] As an embodiment of the present invention, the calculation of the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters includes:

[0108] Based on the slope parameters, determining the sliding body and sliding surface of the high slope along the layer;

[0109] Dividing the sliding body according to a preset dividing angle to obtain sliding body strips;

[0110] Analyze the stress state of the sliding body strip, determine the vertical angle of the sliding surface, and analyze the internal friction angle of the sliding body strip;

[0111] Calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle;

[0112] According to the thrust coefficient of the sliding body, the stress state, the vertical angle and the internal friction angle, the anti-sliding force and the sliding force of the high slope along the layer are calculated using the following formula:

[0113] Z p =d p h p +tanβ p [M p-1 sin(A p +α)

[0114] -M p sin(A p +α)+(1-D S )G p sin A p -D C G p cos A p -T p-1 sin(A p-1 -A p )]

[0115] X p =M p-1 cos(A p +α)-M p cos(A p +α)+(1-D S )G p cos A p

[0116] -D C G p sin A p -T p-1 cos(A p-1 -A p )

[0117]

[0118] Among them, Z represents the anti-sliding force of the high slope along the layer, X represents the sliding force of the high slope along the layer, p represents the pth sliding block of the high slope along the layer, and Z p represents the anti-sliding force of the layer-high slope corresponding to the pth sliding block, X p represents the sliding force of the pth sliding block on the layer height slope, d p represents the cohesion of the pth sliding block of the slope along the layer height, h p represents the height of the pth sliding block corresponding to the layer high slope, β pM represents the internal friction angle of the pth sliding block of the layer high slope. p-1 represents the friction force on the p-1th sliding block along the layer height slope, M p A represents the friction force on the pth sliding block along the layer height slope. p A represents the vertical angle of the layer height slope corresponding to the pth sliding block, p-1 represents the vertical angle of the layer high slope corresponding to the p-1th sliding body block, α represents the division angle of the layer high slope corresponding to the sliding body, D S Denotes the horizontal seismic action coefficient of the high slope along the layer, D C It represents the vertical seismic action coefficient of the high slope along the layer, G p represents the gravity on the pth sliding block of the layer-high slope, T p-1 represents the residual landslide thrust transmitted by the p-1th sliding block corresponding to the high slope of the layer, tan represents the tangent function, sin represents the sine function, cos represents the cosine function, q represents the qth sliding block corresponding to the high slope of the layer, n represents the total number of sliding blocks along the high slope of the layer, γ q represents the sliding thrust coefficient of the qth sliding block along the layer height slope, Z n represents the anti-sliding force of the nth sliding block of the layer high slope, X n It represents the sliding force of the nth sliding block corresponding to the layer height slope.

[0119] The sliding body refers to the part of the rock and soil body that may slide in the high slope along the layer. The sliding surface refers to the dividing line between the sliding body and the stable body. The preset division angle refers to the angle divided in the sliding body, for example, divided according to 30°. The sliding body block refers to the block divided from the sliding body according to the preset division angle. The force state refers to the mechanical state of the sliding body block under the action of various forces. The vertical angle refers to the angle between the sliding surface and the vertical surface. The internal friction angle refers to the maximum friction angle when the sliding body blocks rub against each other. The sliding body thrust coefficient refers to the thrust coefficient used to describe the thrust generated by the slope during the landslide process. The cohesion refers to the force generated by the connection between particles in the rock and soil material through intermolecular forces or cementing materials. The horizontal seismic action coefficient refers to the ratio of the horizontal seismic force on the high slope along the layer to the gravity of the high slope along the layer under the action of earthquake. The vertical seismic action coefficient refers to the ratio of the vertical seismic force on the high slope along the layer to the gravity of the high slope along the layer under the action of earthquake. The residual landslide thrust refers to the residual force of the sliding body on the landslide body after the slope landslide occurs.

[0120] Optionally, the calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle includes:

[0121] Obtaining the slider length of the slider;

[0122] According to the vertical angle, internal friction angle and length of the sliding block, the sliding body thrust coefficient of the sliding block is calculated using the following formula:

[0123]

[0124] Among them, γ p A represents the sliding thrust coefficient of the p-th sliding block corresponding to the high slope of the layer, p represents the p-th sliding block corresponding to the high slope of the layer, and p-1 A represents the vertical angle of the layer-high slope corresponding to the p-1th sliding block, p represents the vertical angle of the layer-high slope corresponding to the pth sliding block, L p-1 represents the length of the sliding block corresponding to the p-1th sliding block along the layer height slope, L p represents the length of the sliding block corresponding to the pth sliding block along the high slope, α represents the division angle of the sliding block corresponding to the high slope along the layer, β p-1 represents the internal friction angle of the p-1th sliding block of the layer high slope, β p It represents the internal friction angle of the pth sliding block corresponding to the layer height slope, tan represents the tangent function, sin represents the sine function, and cos represents the cosine function.

[0125] The embodiment of the present invention constructs a slope state model of the high slope along the layer based on the anti-sliding force, sliding force and slope parameters, so as to better understand and analyze the slope state and stability of the high slope along the layer, wherein the slope state model refers to a mathematical or physical model used to describe and analyze the stability of the slope under specific conditions.

[0126] Optionally, as an embodiment of the present invention, the slope state model of the high slope along the layer based on the anti-sliding force, the sliding force and the slope parameters can be constructed by a physical model test method. The physical model test refers to a physical model test conducted in a laboratory or on-site to simulate the state of the actual high slope along the layer.

[0127] S4. Based on the slope state model, determine the slope state of the high slope along the layer, identify the influencing factors that affect the slope state, construct a relationship curve between the slope state and the influencing factors, and based on the relationship curve, construct a slope state influence model of the high slope along the layer, couple the slope state influence model, the slope state model and the deformation analysis model to obtain a slope displacement analysis model.

[0128] The embodiment of the present invention can accurately determine the current state of the high slope along the layer by determining the slope state of the high slope along the layer based on the slope state model. The slope state refers to the description of the physical and mechanical state of the high slope along the layer at a specific time or under specific conditions, such as stability, deformation characteristics, stress distribution, displacement, etc.

[0129] The embodiment of the present invention can strengthen the monitoring of these factors in a targeted manner by identifying the influencing factors that affect the state of the slope, and timely discover potential signs of instability. The influencing factors refer to various natural and human factors that can have a direct or indirect impact on the stability of the slope, such as precipitation, groundwater, temperature, and blasting.

[0130] The embodiment of the present invention can make the analysis of the factors affecting slope stability more accurate and specific by constructing the relationship curve between the slope state and the influencing factors. The relationship curve refers to a chart drawn by a mathematical model or statistical analysis method when analyzing the relationship between the slope state and the influencing factors, which shows the trend of the slope state changing with the change of one or more influencing factors.

[0131] As an embodiment of the present invention, the step of constructing a relationship curve between the slope state and the influencing factor includes:

[0132] Acquiring multi-source data of the slope status and influencing factors;

[0133] Preprocessing the multi-source data to obtain preprocessed multi-source data;

[0134] Constructing a scatter plot of the relationship between the slope state and the influencing factors according to the preprocessed multi-source data;

[0135] Analyzing the distribution of scatter point coordinates of the relationship scatter plot;

[0136] Based on the scatter point coordinate distribution, the relationship scatter point diagram is fitted into a relationship curve.

[0137] The multi-source data refers to data from different sources and different types, including ground monitoring data, remote sensing data, geological survey data, meteorological data, hydrological data, etc. The pre-processed multi-source data refers to data after cleaning, conversion, standardization, etc. of the multi-source data. The relationship scatter plot refers to a statistical chart used to show the relationship between two variables. The scatter coordinate distribution refers to the coordinate position of each data point in the relationship scatter plot.

[0138] The embodiment of the present invention can more accurately predict the stability of the slope under different conditions by constructing the slope state influence model of the layered high slope based on the relationship curve, and provide a scientific basis for preventing landslides. The slope state influence model refers to a quantitative influence model for describing and analyzing various natural and human factors (influencing factors) on the slope stability state.

[0139] As an embodiment of the present invention, the slope state influence model of the layer-by-layer high slope is constructed based on the relationship curve, including:

[0140] Acquire multiple groups of relationship data of the relationship curve;

[0141] Based on the relationship curve, a linear regression model of the layer high slope is constructed;

[0142] According to the multiple sets of relationship data, the slope parameter and intercept parameter of the linear regression model are calculated using the following formula:

[0143]

[0144]

[0145] Among them, k represents the slope parameter, b represents the intercept parameter, m represents the number of groups of multiple relationship data, j represents the jth group of data corresponding to multiple relationship data, and I j Indicates that multiple sets of relationship data correspond to the jth group of impact factors, Q j Indicates the slope state of multiple sets of relationship data corresponding to the jth set;

[0146] The linear regression model is updated by using the model parameters to obtain the slope state influence model of the layer-by-layer high slope.

[0147] Wherein, the multiple sets of relationship data refer to a series of data points obtained from the relationship curve, each data point contains multiple independent variables (influencing factors) and a dependent variable (slope state). The linear regression model refers to a linear statistical model. The model parameters refer to the coefficients in the linear regression model, i.e., the slope parameter and the intercept parameter.

[0148] The embodiment of the present invention couples the slope state influence model, the slope state model and the deformation analysis model to obtain a slope displacement analysis model, which can provide a more comprehensive consideration of topographic, geological, hydrological and environmental factors, thereby more accurately predicting the displacement of the slope. The slope displacement analysis model refers to a mathematical model for describing and analyzing the displacement behavior of a slope under the influence of natural and human factors over time.

[0149] As an embodiment of the present invention, the slope state influence model, the slope state model and the deformation analysis model are coupled to obtain the slope displacement analysis model, including:

[0150] Determining a common data format of the slope state influence model, the slope state model, and the deformation analysis model;

[0151] Based on the data format, a data exchange mechanism for the slope state influence model, the slope state model and the deformation analysis model is constructed;

[0152] Constructing a model integration architecture for managing data flow between the slope state impact model, the slope state model, and the deformation analysis model;

[0153] According to the model integration architecture, a coupling mechanism of the slope state influence model, the slope state model and the deformation analysis model is constructed;

[0154] The slope state influence model, the slope state model and the deformation analysis model are coupled through the data exchange mechanism, the model integration architecture and the coupling mechanism to obtain an initial coupling model;

[0155] Obtaining model prediction values ​​and actual observation values ​​of the initial coupling model;

[0156] Calculating a mean square error of the initial coupling model based on the model prediction value and the actual observation value;

[0157] When the average error meets a preset error threshold, the initial coupling model is used as a slope displacement analysis model.

[0158] Among them, the data format refers to a standardized format for storing and transmitting data to ensure data consistency and interoperability, and the data format includes: data type, data structure, data unit and data range. The data exchange mechanism refers to a mechanism for exchanging data between models to ensure that data can be accurately and efficiently transmitted between models. The model integration architecture refers to a structure or framework for managing and coordinating data flow between models. The coupling mechanism refers to a way to combine different models together to ensure that different models can work together. The initial coupling model refers to the first coupling model constructed, which may require further optimization and adjustment. The model prediction value refers to the slope displacement value predicted by the initial coupling model based on the input data. The actual observation value refers to the slope displacement value obtained by actual monitoring or measurement. The mean square error refers to a method of measuring the model prediction error, which calculates the average of the squares of the differences between the model prediction value and the actual observation value. The preset error threshold refers to a pre-set error standard. The mean square error between the model prediction value and the actual observation value must be lower than this threshold before the model is considered valid.

[0159] Optionally, the data exchange mechanism for constructing the slope state influence model, slope state model and deformation analysis model based on the data format can be constructed by constructing a data exchange interface between models, wherein the data exchange interface includes: a file interface, a database interface and a network communication interface.

[0160] Optionally, according to the model integration architecture, the coupling mechanism of the slope state impact model, the slope state model and the deformation analysis model can be constructed by using a message queue middleware (such as RabbitMQ, ActiveMQ) or an event-driven middleware (such as Kafka) to manage asynchronous communication between models, thereby constructing the coupling mechanism.

[0161] S5. Based on the slope deformation data, the displacement-time analysis curve of the high slope along the layer is analyzed by using the slope displacement analysis model. Based on the displacement-time analysis curve, the displacement anomaly coefficient of the high slope along the layer is calculated. According to the displacement anomaly coefficient, an early warning mechanism of the high slope along the layer is constructed. Based on the early warning mechanism, intelligent monitoring and early warning of the high slope along the layer is performed.

[0162] The embodiment of the present invention can predict the displacement trend of the high slope along the layer by analyzing the displacement-time analysis curve of the high slope along the layer based on the slope deformation data and using the slope displacement analysis model, which is helpful to understand the future displacement change of the slope. The displacement-time analysis curve refers to a curve chart showing the displacement change of the slope within a certain period of time.

[0163] Furthermore, the embodiment of the present invention can identify the abnormal situation of slope displacement by calculating the displacement anomaly coefficient of the layer high slope based on the displacement-time analysis curve, thereby issuing an early warning in time. The displacement anomaly coefficient refers to an indicator used to evaluate whether the displacement of the layer high slope deviates from the normal range.

[0164] As an embodiment of the present invention, the calculation of the displacement anomaly coefficient of the layer-by-layer high slope based on the displacement-time analysis curve includes:

[0165] Determining the reference displacement of the layer-by-layer high slope;

[0166] Calculating the mean and standard deviation of the reference displacement;

[0167] Based on the displacement-time analysis curve, obtaining the displacement values ​​of the layered high slope at different time points;

[0168] According to the average value and standard deviation of the benchmark displacement and the displacement value of the layer high slope, the displacement anomaly coefficient of the layer high slope is calculated using the following formula:

[0169]

[0170] Where ω represents the displacement anomaly coefficient, W t It represents the displacement value of the layer high slope at different time points, represents the average value of the reference displacement, Indicates the standard deviation of the reference displacement.

[0171] The reference displacement refers to the reference displacement used to compare and evaluate displacement anomalies in the displacement-time analysis curve, and is determined based on the changing trend and stability of the displacement over a period of time to ensure a relatively stable reference point during the analysis process.

[0172] The embodiment of the present invention can issue a warning signal in time by constructing the early warning mechanism of the layer-by-layer high slope according to the displacement anomaly coefficient, reminding relevant personnel to pay attention to the potential risks of the slope. The early warning mechanism refers to a system or method based on specific indicators or conditions for predicting and responding to potential dangerous situations in time.

[0173] Optionally, as an embodiment of the present invention, the early warning mechanism for the layer-by-layer high slope constructed according to the displacement anomaly coefficient can be constructed through big data processing technology, wherein the big data processing technology refers to a series of technologies and tools for processing large-scale data sets.

[0174] The embodiment of the present invention can achieve early warning of abnormal displacement of the high slope along the layer by executing the intelligent monitoring and early warning of the high slope along the layer based on the early warning mechanism, thereby more effectively managing the safety risks of the slope.

[0175] The embodiment of the present invention can more effectively monitor the displacement and stress changes of the slope by determining the monitoring points and reference points of the high slope along the layer based on the topographic map and landform features; optionally, the embodiment of the present invention can realize real-time, continuous and high-precision monitoring of the high slope along the layer by constructing the Beidou monitoring module of the monitoring point; the embodiment of the present invention can quantitatively evaluate the performance of the monitoring system by calculating the monitoring reliability coefficient of the Beidou monitoring module, thereby ensuring the quality of monitoring data and the reliability of the system; the embodiment of the present invention can evaluate the risk level of the high slope along the layer by calculating the anti-sliding force and sliding force of the high slope along the layer based on the slope parameters; the embodiment of the present invention can make the analysis of the influencing factors of the slope stability more accurate and specific by constructing the relationship curve between the slope state and the influencing factors, and finally, the embodiment of the present invention can identify the abnormal situation of the slope displacement by calculating the displacement anomaly coefficient of the high slope along the layer based on the displacement-time analysis curve, thereby issuing an early warning in time. Therefore, the present invention can improve the monitoring efficiency of the high slope along the layer.

[0176] like Figure 2 As shown, it is a functional module diagram of a Beidou monitoring system for high slopes along the layer provided by an embodiment of the present invention.

[0177] The Beidou monitoring system 200 for high slopes along the layers of the present invention can be installed in an electronic device. According to the functions to be implemented, the Beidou monitoring system 200 for high slopes along the layers of the present invention can include a Beidou monitoring construction module 201, a deformation analysis model construction module 202, a slope state model construction module 203, a slope displacement analysis model construction module 204 and an intelligent monitoring and early warning module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0178] In this embodiment, the functions of each module / unit are as follows:

[0179] The Beidou monitoring construction module 201 is used to analyze the topographic map and geomorphic features of the high slope along the layer, determine the monitoring points and reference points of the high slope along the layer based on the topographic map and geomorphic features, and construct the Beidou monitoring module of the monitoring point;

[0180] The deformation analysis model building module 202 is used to calculate the monitoring reliability coefficient of the Beidou monitoring module. When the monitoring reliability coefficient meets the preset reliability coefficient, the Beidou monitoring module and the reference point are used to monitor the slope deformation data of the monitoring point in real time, and the deformation analysis model of the layer-by-layer high slope is built based on the slope deformation data.

[0181] The slope state model building module 203 is used to obtain the slope parameters of the high slope along the layer, calculate the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters, and build the slope state model of the high slope along the layer based on the anti-sliding force, the sliding force and the slope parameters;

[0182] The slope displacement analysis model building module 204 is used to determine the slope state of the layer-adjacent high slope based on the slope state model, identify the influencing factors affecting the slope state, build a relationship curve between the slope state and the influencing factors, build a slope state influence model of the layer-adjacent high slope based on the relationship curve, and couple the slope state influence model, the slope state model and the deformation analysis model to obtain a slope displacement analysis model;

[0183] The intelligent monitoring and early warning module 205 is used to analyze the displacement-time analysis curve of the high slope along the layer based on the slope deformation data and the slope displacement analysis model, calculate the displacement anomaly coefficient of the high slope along the layer based on the displacement-time analysis curve, construct an early warning mechanism for the high slope along the layer according to the displacement anomaly coefficient, and perform intelligent monitoring and early warning of the high slope along the layer based on the early warning mechanism.

[0184] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A Beidou monitoring method for high slopes along the layer, characterized in that: The method comprises: Analyze the topographic map and geomorphic features of the high slope along the layer, determine the monitoring points and reference points of the high slope along the layer based on the topographic map and geomorphic features, and construct a Beidou monitoring module for the monitoring points; Calculating the monitoring reliability coefficient of the Beidou monitoring module, and when the monitoring reliability coefficient meets the preset reliability coefficient, using the Beidou monitoring module and the reference point to monitor the slope deformation data of the monitoring point in real time, and constructing the deformation analysis model of the layer-by-layer high slope based on the slope deformation data; Obtaining slope parameters of the high slope along the layer, calculating the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters, and constructing a slope state model of the high slope along the layer based on the anti-sliding force, the sliding force and the slope parameters; Based on the slope state model, determine the slope state of the high slope along the layer, identify the influencing factors that affect the slope state, construct a relationship curve between the slope state and the influencing factors, and based on the relationship curve, construct a slope state influence model of the high slope along the layer, and couple the slope state influence model, the slope state model and the deformation analysis model to obtain a slope displacement analysis model; Based on the slope deformation data, the displacement-time analysis curve of the high slope along the layer is analyzed using the slope displacement analysis model. Based on the displacement-time analysis curve, the displacement anomaly coefficient of the high slope along the layer is calculated. According to the displacement anomaly coefficient, an early warning mechanism for the high slope along the layer is constructed. Based on the early warning mechanism, intelligent monitoring and early warning of the high slope along the layer is performed.

2. The Beidou monitoring method for high slopes along the layer as claimed in claim 1 is characterized in that: Determining the monitoring points and reference points of the high slope along the layer based on the topographic map and landform features includes: Based on the topographic map and geomorphic features, a geological survey is conducted on the high slope along the layer to obtain the geological structure of the slope; Analyze the potential landslide risk area and relatively stable area of ​​the high slope along the layer based on the geological structure of the slope; Determining monitoring points of the high slope along the layer according to the potential landslide risk area; According to the relatively stable area, the reference point of the layer-adjacent high slope is determined.

3. The Beidou monitoring method for high slopes along the layer as claimed in claim 1 is characterized in that: The Beidou monitoring module for constructing the monitoring point includes: Determine the number of monitoring points and the distribution of monitoring points of the monitoring points; According to the number and distribution of the monitoring points, configure Beidou receivers and sensors at the monitoring points; Constructing a data acquisition unit and a communication module for the monitoring point; Integrate the Beidou receiver, sensor, data acquisition unit and communication module into a monitoring point module; Analyzing the location environment of the monitoring point, and configuring the support rod of the monitoring point module based on the location environment; Constructing a fixing unit of the support rod according to the geomorphic features corresponding to the monitoring point; The Beidou monitoring module of the monitoring point is constructed by means of the monitoring point module, the support rod and the fixing unit.

4. The Beidou monitoring method for high slopes along the layer as claimed in claim 1 is characterized in that: The calculating of the monitoring reliability coefficient of the Beidou monitoring module comprises: Testing the Beidou monitoring module to obtain multiple groups of test data; According to the multiple groups of test data, the Beidou monitoring module collects multiple groups of actual data of the same number of monitoring points; The monitoring reliability coefficient of the Beidou monitoring module is calculated based on the multiple groups of test data and the multiple groups of actual data.

5. The Beidou monitoring method for high slopes along the layer as claimed in claim 1, characterized in that: The step of calculating the anti-sliding force and the sliding force of the high slope along the layer based on the slope parameters includes: Based on the slope parameters, determining the sliding body and sliding surface of the high slope along the layer; Dividing the sliding body according to a preset dividing angle to obtain sliding body strips; Analyze the stress state of the sliding body strip, determine the vertical angle of the sliding surface, and analyze the internal friction angle of the sliding body strip; Calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle; The anti-sliding force and the sliding force of the high slope along the layer are calculated according to the thrust coefficient of the sliding body, the stress state, the vertical angle and the internal friction angle.

6. The Beidou monitoring method for high slopes along the layer as claimed in claim 5 is characterized in that: The step of calculating the sliding body thrust coefficient of the sliding body block according to the vertical angle and the internal friction angle comprises: Obtaining the slider length of the slider; The sliding body thrust coefficient of the sliding body block is calculated according to the vertical angle, the internal friction angle and the length of the sliding body block.

7. The Beidou monitoring method for high slopes along the layer as claimed in claim 1, characterized in that: The step of constructing the slope state influence model of the layer-by-layer high slope based on the relationship curve includes: Acquire multiple groups of relationship data of the relationship curve; Based on the relationship curve, a linear regression model of the layer high slope is constructed; Calculating the slope parameter and intercept parameter of the linear regression model according to the multiple sets of relationship data; The linear regression model is updated by using the slope parameter and the intercept parameter to obtain the slope state influence model of the layer-by-layer high slope.

8. The Beidou monitoring method for high slopes along the layer as claimed in claim 1, characterized in that: The slope state influence model, the slope state model and the deformation analysis model are coupled to obtain a slope displacement analysis model, including: Determining a common data format of the slope state influence model, the slope state model, and the deformation analysis model; Based on the data format, a data exchange mechanism for the slope state influence model, the slope state model and the deformation analysis model is constructed; Constructing a model integration architecture for managing data flow between the slope state impact model, the slope state model, and the deformation analysis model; According to the model integration architecture, a coupling mechanism of the slope state influence model, the slope state model and the deformation analysis model is constructed; The slope state influence model, the slope state model and the deformation analysis model are coupled through the data exchange mechanism, the model integration architecture and the coupling mechanism to obtain an initial coupling model; Obtaining model prediction values ​​and actual observation values ​​of the initial coupling model; Calculating a mean square error of the initial coupling model based on the model prediction value and the actual observation value; When the mean square error meets a preset error threshold, the initial coupling model is used as a slope displacement analysis model.

9. The Beidou monitoring method for high slopes along the layer as claimed in claim 1, characterized in that: The calculation of the displacement anomaly coefficient of the layer-by-layer high slope based on the displacement-time analysis curve includes: Determining the reference displacement of the layer-by-layer high slope; Calculating the mean and standard deviation of the reference displacement; Based on the displacement-time analysis curve, obtaining the displacement values ​​of the layered high slope at different time points; The displacement anomaly coefficient of the high slope along the layer is calculated according to the average value and standard deviation of the reference displacement and the displacement value of the high slope along the layer.

10. The Beidou monitoring method for high slopes along the layer as claimed in claim 1, characterized in that: The step of constructing a relationship curve between the slope state and the influencing factors includes: Acquiring multi-source data of the slope status and influencing factors; Preprocessing the multi-source data to obtain preprocessed multi-source data; Constructing a scatter plot of the relationship between the slope state and the influencing factors according to the preprocessed multi-source data; Analyzing the distribution of scatter point coordinates of the relationship scatter plot; Based on the scatter point coordinate distribution, the relationship scatter point diagram is fitted into a relationship curve.

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