Intelligent slope monitoring and early warning cloud platform based on multi-source data fusion

Abstract

The invention discloses a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion, and belongs to the field of slope intelligent monitoring. Wherein the slope monitoring system is used for monitoring a slope rock mass to obtain monitoring data; the data cloud platform is used for acquiring monitoring data and generating an independent analysis result according to the monitoring data; the control platform is used for controlling data transmission, working states and the like of the monitoring system; the early warning system is used for acquiring the analysis result and performing early warning feedback according to the analysis result; and the user side is used for receiving the early warning notification, feeding back an alarm processing result, carrying out two-way interaction between the platform and the site and realizing an early warning information feedback closed loop. According to the scheme, different types of data of an existing strip mine independent monitoring system are intelligently optimized and fused, comprehensive analysis and judgment are carried out, and technical support is provided for strip mine slope safety.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent slope monitoring, and in particular to an intelligent slope monitoring and early warning cloud platform based on multi-source data fusion. Background Art

[0002] The safety of open-pit mine slopes has always attracted much attention. With the rapid development of social industry, large-scale high-steep rock slope projects have become normalized, and open-pit mining is moving towards informatization and intelligence. Traditional slope monitoring methods are restricted by limited monitoring points and single monitoring methods. Each monitoring method is independent of each other. Although it targets the same slope safety issue, it is difficult to integrate various monitoring data to analyze the open-pit mine as a whole, and it is impossible to fully capture the dynamic changes of the slope, resulting in a large number of safety monitoring equipment and personnel invested in open-pit mines, but with little effect.

[0003] The patent [A slope monitoring and early warning system, CN 115050163 A] introduces a slope monitoring and early warning system, including a monitoring subsystem, a data platform and an early warning subsystem, including surface displacement, deep displacement, hydrological monitoring, support structure stress monitoring, etc., but does not consider deep microseismic monitoring of the slope and blasting vibration monitoring. In response to the above technical problems, this solution proposes a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion. This solution improves the comprehensiveness, real-time and accuracy of slope monitoring, and provides an innovative solution for geological disaster prevention. Summary of the invention

[0004] In order to solve the shortcomings of the above technologies, the present invention aims to provide a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A slope intelligent monitoring and early warning cloud platform based on multi-source data fusion, comprising a slope monitoring system, a data cloud platform, a control platform, an early warning cloud platform, and a user terminal; the slope monitoring system is used to monitor the slope rock mass to obtain monitoring data; the data cloud platform is used to obtain the monitoring data and generate independent analysis results based on the monitoring data; the control platform is used to control the data transmission, equipment status, and parameter adjustment of the slope monitoring system; the early warning cloud platform is used to obtain the analysis results, and provide early warning feedback and reasonable suggestions based on the analysis results; the user terminal is used to receive early warning notifications, feedback alarm processing results, conduct two-way interaction between the platform and the site, and realize a closed loop of early warning information feedback.

[0007] Furthermore, the slope monitoring system includes a deep microseismic monitoring module, a deep displacement monitoring module, a groundwater monitoring module, a surface displacement monitoring module, a structural stress monitoring module, a blasting vibration monitoring module, an image data acquisition module, and a geological data acquisition module, and data is collected and transmitted independently between each module.

[0008] Furthermore, except for manual monitoring data, all other monitoring data can be automatically collected and transmitted to the data cloud platform.

[0009] Furthermore, the deep microseismic monitoring module adopts an all-fiber microseismic monitoring system, including a digital signal processing system, an acquisition system, an acceleration sensor, an optical cable, a data communication modem, and a source positioning software. The digital signal processing system is used to interpret the optical fiber microseismic signal and convert it into a digital signal, the acquisition system is used to collect and store the microseismic signal, the acceleration sensor is used to receive the microseismic signal, the optical cable is used to transmit the microseismic signal received by the acceleration sensor, the data communication modem is used to convert the digital signal into an analog signal for easy transmission, and the source positioning software is used to obtain the source parameters carried by the microseismic waveform. The deep microseismic monitoring module obtains microseismic data, including the coordinates, energy, magnitude, and stress drop of the source.

[0010] Furthermore, the deep displacement monitoring module adopts an inclinometer, and adds a GPS positioning device on the top of the inclinometer to realize settlement monitoring, and adopts a multi-hole joint arrangement to form a three-dimensional monitoring network to obtain deep rock displacement data.

[0011] Furthermore, the surface displacement monitoring module includes a ground laser radar, a GNSS displacement monitoring station, and an artificial monitoring pile, which are used to obtain slope radar data, GNSS displacement data, and artificial pile monitoring data, respectively.

[0012] Furthermore, the underground hydrological monitoring module includes at least one of the monitoring parameters in the hydrological parameter monitoring, at least a dual-hole joint arrangement, and acquires underground hydrological data.

[0013] Furthermore, the structural stress monitoring module includes at least one of stress gauges for monitoring the stress of anchor rods, anchor cables, and reinforced concrete structures.

[0014] Furthermore, the blasting vibration monitoring module is used to monitor particle vibration velocity, adopts a dedicated three-way vector sensor, and obtains blasting vibration data.

[0015] Furthermore, the image data acquisition module is used to obtain slope morphological features, including but not limited to drone aerial photography, laser scanning and other acquisition methods, and obtain slope image data.

[0016] Furthermore, the geological data collection module mainly includes the collation of geological exploration and production exposure data, including a wide range of geology within the entire mining cycle.

[0017] Furthermore, the monitoring data includes deep microseismic data, deep displacement data, surface displacement data, groundwater data, stress data of slope support structures, blasting vibration data, slope image data, and slope geological and geographical data.

[0018] Furthermore, the surface displacement data includes slope radar data, GNSS displacement data, and artificial pile monitoring data.

[0019] Furthermore, the slope image data includes drone aerial photography slope morphology data, three-dimensional laser scanning data, and oblique image mapping data.

[0020] Furthermore, the slope geological and geographical data include faults, joints, lithology, veins and other geological and geographical data.

[0021] Furthermore, the data cloud platform includes a data recording module, a data input module, a data processing module and a data analysis module. The data recording module is used to store the acquired slope monitoring data and establish a multi-model large database. The data input module is used to manually input slope manual monitoring data. The data processing module is used to process the stored slope monitoring data. The data analysis module is used to generate the analysis results for the processed slope monitoring data.

[0022] Furthermore, the multi-model big database refers to the integration of different types of database models into an integrated backend, including monitoring data, analyzed result data, and early warning data. The database is structured based on time and space elements, including but not limited to data on the same slope, and is used as the data basis for the calculation of the early warning cloud platform.

[0023] Furthermore, the data processing module is used to correct, integrate and process the original monitoring data in the data recording module, and finally generate a visualization file to form the analysis results of a single monitoring module, which are uploaded to the early warning cloud platform as the basic data of a single monitoring and early warning module, and the analysis results are incorporated into the multi-model large database.

[0024] Furthermore, the data analysis module includes statistical analysis and multi-source fusion analysis of the monitoring data. The statistical analysis includes time-frequency analysis of microseismic events, and automatic identification and analysis of the spatial aggregation patterns of earthquake source points through automatic detection and recognition algorithms to obtain spatial aggregation parameters and their spatial relationship with geological structures. The statistical analysis also includes time-frequency characteristics and spatial characteristics of displacement and water pressure changes.

[0025] Furthermore, the multi-source fusion analysis includes the "time-space-strength" feature fusion analysis of the monitoring data and the three-dimensional stereo analysis of the "point-line-surface-body" fusion in the spatial dimension of the displacement data, and predicts the change trend of the slope rock in a certain period of time thereafter.

[0026] Furthermore, the analysis results include single monitoring module analysis results, statistical analysis results, and multi-source data fusion analysis results. The multi-source data fusion analysis results are based on data fusion of optional monitoring modules to meet different on-site needs.

[0027] Furthermore, the early warning cloud platform includes a single monitoring and early warning module, a comprehensive information early warning module, an early warning model optimization module, and a geological image model module. The single monitoring and early warning module is used to provide information feedback based on the analysis results of the single monitoring module, and the comprehensive information early warning module is used to provide information feedback based on the multi-source data fusion analysis results. The feedback results are intuitively displayed in combination with the three-dimensional geological image model.

[0028] Furthermore, the early warning model optimization module is used to optimize the control standard of the control platform and the prediction and evaluation method of the slope intelligent monitoring and early warning cloud platform according to the analysis results.

[0029] Furthermore, the geological image model module is used to intuitively display a three-dimensional transparent slope geological image model formed by the slope image data and the slope geological and geographical data, so as to clearly display the layout of the monitoring equipment and realize the visualization of the monitoring results.

[0030] Furthermore, the user terminal is used to receive early warning notifications, feedback alarm processing results, and conduct two-way interaction between the platform and the site. The user terminal will notify the on-site management personnel of the early warning information reviewed by professionals, obtain feedback information after on-site confirmation and upload it to the early warning cloud platform, thus realizing a closed loop of early warning information feedback.

[0031] Furthermore, the present invention provides an inclinometer, which includes a GPS positioning device, monitoring points and an inclinometer connecting rod; the monitoring points are evenly distributed on the inclinometer connecting rod, the monitoring point at the top is set as the monitoring zero point, and the GPS positioning device is embedded in the top monitoring point of the inclinometer.

[0032] Furthermore, the slope displacement monitoring and early warning method comprises the following steps: (1) Before the inclinometer is installed, a GPS positioning device is embedded in the top monitoring point of the inclinometer connecting rod, namely, the monitoring zero point. When the inclinometer is installed, the monitoring zero point is located on the ground surface, and the GPS positioning device automatically obtains the real-time dynamic three-dimensional coordinate information of the monitoring zero point. When the monitoring zero point is used as the reference point, the problem that the coordinates of the reference point change in the traditional method causes the data of other monitoring points to be inconsistent with the actual situation is overcome. (2) Based on the measurement data of the deep part of the landslide obtained by the inclinometer, these measurement data are converted according to the monitoring zero point, and the displacement in the effective deep X, Y, and Z directions and the accumulated total displacement are automatically obtained to obtain the actual displacement and movement trend of each monitoring point in the deep part of the slope. (3) Compare with the proposed early warning threshold. If the early warning threshold is exceeded, the system will automatically issue an early warning message so that the on-site construction personnel can make a judgment and evacuate in time.

[0033] The step (2): select the monitored area, plan the layout of the monitoring points 5-2, drill holes in the slope, install the inclinometer 5-5, the system automatically obtains the initial coordinates D0 (X0, Y0, Z0) of the monitoring zero point, records the distance and burial depth of each monitoring point 5-2, and then calculates the coordinates of other monitoring points 5-2 by the angle displacement principle. When the slope is displaced, the inclinometer 5-5 moves with the rock and soil body. At this time, the inclinometer 5-5 automatically reads the angle of displacement of each monitoring point 5-2, and uses the GPS positioning device 5-1 on the top to automatically obtain the real-time coordinates D'0 of the monitoring zero point, and then uses the angle displacement principle to calculate the actual displacement of the deep part of the slope body in the X, Y, and Z directions.

[0034] The specific calculation steps are as follows:

[0035] First, according to the initial coordinate D0 of the monitoring zero point, each monitoring point D i Coordinate (X Di , Y Di , Z Di ), when the slope slides, the system automatically obtains the real-time coordinates of the monitoring zero point D'0 (X'0, Y0', Z0'), and uses the real-time coordinates of the monitoring zero point D'0 as the reference point to calculate the real-time coordinates of other monitoring points The coordinates of other monitoring points are calculated according to the following formula:

[0036]

[0037] Among them, L i is the length of the i-th connecting rod (3), L xi For L i Projection on the XZ plane, α xi For L xi The angle with the positive direction of the Z coordinate axis, Lyi For L i Projection on the YZ plane, α iy For L yi The angle with the positive direction of the Z coordinate axis, α zi For L i The angle between the positive direction of the Z coordinate axis and the X, Y, and Z directions of each monitoring point is calculated:

[0038]

[0039] Finally, it is concluded that each monitoring point is horizontal and vertical The magnitude of displacement in the direction:

[0040]

[0041] Compared with the prior art, the beneficial effect of the present invention is that the present invention provides a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion, including a slope monitoring system, a data cloud platform, a control platform and an early warning cloud platform. By introducing a variety of sensors such as microseismic, inclinometer, GNSS, and lidar, the system obtains multi-dimensional data such as microseismic, displacement, and hydrology in real time. Advanced data fusion and processing algorithms are used to integrate information from various sensors, and deep learning and machine learning technologies are used for efficient analysis. The cloud platform architecture realizes real-time data uploading, storage, and management, and provides users with remote access and monitoring services. Based on the cloud data analysis results, the intelligent early warning cloud platform predicts slope stability through real-time monitoring and issues early warnings. The user interface is friendly and intuitive, allowing relevant managers to view monitoring data, early warning information, and geological disaster risk assessment reports in real time. This patented technical solution comprehensively improves the comprehensiveness, real-timeness, and accuracy of slope monitoring, and provides innovative solutions for geological disaster prevention and urban planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural block diagram of a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion disclosed in a preferred embodiment of the present invention;

[0043] Figure 2 is a structural block diagram of a slope monitoring system of a specific embodiment of the present invention;

[0044] Figure 3 A schematic cross-sectional layout diagram of a slope monitoring system according to a specific embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the plan layout of a slope monitoring system of a specific embodiment of the present invention;

[0046] Figure 5 This is the working flow diagram of the inclinometer.

[0047] Figure 6 Inclinometer device.

[0048] Figure 7 This is a profile of a slope in the inclinometer monitoring area.

[0049] Figure 8 This is the cross-section diagram of a slope after displacement.

[0050] Fig. 9 This is the principle diagram of angle-displacement calculation.

[0051] Fig.10 A side slope profile.

[0052] Description of Reference Numerals

[0053] 1 Slope; 2 GNSS displacement monitoring station; 3 anchor rod; 4 anchor cable; 5 inclinometer hole; 6 hydrological monitoring hole; 7 microseismic sensor; 8 artificial monitoring pile; 9 reinforced concrete retaining wall; 10 UAV; 11 ground lidar; 12 GNSS displacement monitoring satellite; 13 blasting vibrometer; 5-1 GPS positioning device; 5-2 monitoring point; 5-3 inclinometer connecting rod; 5-4 original slope; 5-5 inclinometer. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] In the following description, many specific details are set forth in order to fully understand the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] The present invention is described in detail in conjunction with the schematic diagram of the layout of the slope monitoring system. When describing the embodiments of the present invention in detail, for the sake of convenience of explanation, the cross-sectional diagram of the slope structure and the monitoring facilities therein do not have specific spatial position relationships and geometric size characteristics. The schematic diagram is only an example and should not limit the scope of protection of the present invention.

[0057] This embodiment provides a slope intelligent monitoring and early warning cloud platform based on multi-source data fusion, such as Figure 1As shown, it includes a slope monitoring system, a data cloud platform, a control platform, an early warning cloud platform, and a user terminal. The slope monitoring system is used to monitor the slope rock mass to obtain monitoring data; the data cloud platform is used to obtain the monitoring data and generate independent analysis results based on the monitoring data; the control platform is used to control the data transmission, working status, parameter adjustment, etc. of the monitoring system; the early warning cloud platform is used to obtain the analysis results, and provide early warning feedback and reasonable suggestions based on the analysis results; the user terminal is used to receive early warning notifications in real time, notify on-site staff, feedback alarm processing results, conduct two-way interaction between the platform and the site, and realize a closed loop of early warning information feedback.

[0058] The slope monitoring system includes a deep microseismic monitoring module, a deep displacement monitoring module, a groundwater hydrological monitoring module, a surface displacement monitoring module, a structural stress monitoring module, and a blasting vibration monitoring module.

[0059] In a specific embodiment, the deep microseismic monitoring module refers to a high-sensitivity all-fiber microseismic monitoring system, including a digital signal processing system, an acquisition system, an acceleration sensor, an optical cable, a data communication modem and a source positioning software. The digital signal processing system is used to interpret the optical fiber microseismic signal and convert it into a digital signal. The acquisition system is used to collect and store the microseismic signal. The acceleration sensor is used to receive the microseismic signal. The optical cable is used to transmit the microseismic signal received by the acceleration sensor. The data communication modem is used to convert the digital signal into an analog signal that is convenient for transmission. The source positioning software is used to obtain the source parameters carried by the microseismic waveform. The frequency coverage range of the microseismic signal of the all-fiber microseismic monitoring system is 20Hz to 3kHz, and the dynamic range is 100dB. The signal transmission distance between the microseismic sensor 7 and the microseismic monitoring host can reach 20 kilometers, making the monitoring network layout more flexible. A single monitoring host supports up to 32 channels to be connected at the same time. The acceleration sensor should cover the monitored target area in the spatial dimension, and the spatial layout parameters of the sensor are determined according to the effective range of the sensor signal. The layout points of the microseismic sensor 7 (see Figure 4 ) The monitoring range should be comprehensively considered, and the spacing and burial depth should be reasonably arranged. The microseismic sensors 7 are scattered in three-dimensional space to effectively cover the monitoring area and realize large-scale regional monitoring of mines. The collection of microseismic waveforms is continuous, screened by recognition algorithms, initially positioned by positioning algorithms, manually calibrated, and then uploaded to the system.

[0060] In a specific embodiment, the deep displacement monitoring module refers to an inclinometer, which is composed of a series of continuously connected MEMS (micro-electromechanical system) acceleration sensors. The system can automatically determine the spatial form of each sensor unit, thereby realizing deformation monitoring of the target object, and adding a GPS positioning device to realize settlement monitoring. The inclinometer should be arranged in a multi-hole joint arrangement in the same monitoring area (see Figure 4 )’s inclinometer hole 5 arrangement plan, at least 3 holes are arranged together in the same area to form a joint surface monitoring network composed of “point-line-surface”.

[0061] In a specific embodiment, the underground hydrological monitoring module includes monitoring of hydrological parameters such as underground water level, flow velocity, temperature, etc., and at least one of them is monitored, with the water level as the main parameter. Taking monitoring of underground water level as an example, at least one hydrological survey line is arranged on the slope, and a hydrological survey line adopts a joint arrangement scheme of 6 hydrological monitoring holes with at least 3 holes arranged together. A wireless hydrological monitoring station is used, and data is automatically collected and uploaded.

[0062] In particular, the arrangement of measuring points on the hydrological survey line (hydrological monitoring longitudinal section) (see Figure 4 ), the location and depth of the monitoring point should be determined according to the geological conditions, the location of the potential sliding surface of the mining slope, the possible seepage deformation, the seepage location, the gathering conditions, the amount of seepage, and the monitoring method used. The measuring points should be set in the strong permeable layer as much as possible to monitor the changes in the seepage pressure in each layer. Each longitudinal section should have no less than 3 measuring points at the top, middle and bottom of the slope. The spacing between longitudinal sections should not exceed 100m, and the burial depth should be determined with reference to the actual groundwater level depth. In case of extremely severe weather, the frequency of data collection should be adjusted manually. The allowable error of groundwater level monitoring should comply with relevant national regulations and standards.

[0063] In a specific embodiment, the structure in the structural stress monitoring module refers to a structure for reinforcing and protecting the slope, such as anchor rods 3, anchor cables 4, and reinforced concrete retaining walls 9. The stress monitoring includes but is not limited to at least one of the stress gauges for monitoring the above three structures. The number of stress sensors is designed based on the total number of anchor rods and anchor cables, the spatial dimensions of the reinforced concrete structure, and relevant national specifications and standards. A wireless acquisition system is set up in the same area to realize automatic data collection and uploading.

[0064] In a specific embodiment, the surface displacement monitoring module includes a ground laser radar 11, a GNSS displacement monitoring station 2, and a manual monitoring pile 8. The ground laser radar 11 can monitor the terrace mine slope as a whole. The GNSS displacement monitoring station 2 and the manual monitoring pile 8 are supplements to the radar displacement monitoring of the ground laser radar 11. Due to the slope morphology and monitoring cycle, a simple manual monitoring pile 8 is used for monitoring in places where the GNSS displacement monitoring station 2 is inconvenient to deploy. The manual pile monitoring 8 is economical and convenient, and has low restrictions on the site. The manual pile monitoring 8 is used in places where the information monitoring equipment is insufficient to supplement the surface displacement data. The GNSS displacement monitoring station 2 requires the GNSS displacement monitoring satellite 12 to cooperate in displacement measurement.

[0065] In particular, the inclinometers and GNSS monitoring points should be networked according to the monitoring profiles for overall control. The monitoring profiles should be able to monitor the deformation amount and direction of landslides and collapses, so as to grasp their spatiotemporal dynamic characteristics and identify development trends.

[0066] Specifically, according to actual needs, only one of the surface displacement data and the deep displacement data of the slope can be selectively monitored, and the surface displacement monitoring can selectively select only at least one of the laser radar, GNSS, and manual monitoring piles.

[0067] In a specific embodiment, the blasting vibration monitoring module adopts a blasting vibration meter 13 .

[0068] In a specific embodiment, the slope image data includes the aerial photography slope morphology data of the drone 10, three-dimensional laser scanning data, and oblique image mapping data. The slope morphology data includes the slope morphology such as settlement, deformation cracks, etc. that are constantly changing with the disturbance of engineering excavation. It is necessary to use a drone mounted with a high-definition camera to perform irregular aerial photography scanning and continuous updating. The oblique image mapping data refers to the use of oblique image mapping technology combined with GIS geographic space visualization technology, by carrying multiple sensors on the same flight platform, and collecting images from five different angles such as a vertical, four oblique, etc., to obtain rich high-resolution textures of the mine top and side view. It can truly reflect the ground conditions, obtain the texture information of the mining area geographical scene with high precision, and generate a real geographical image model through advanced positioning, fusion, and model processing technology. Three-dimensional laser scanning data refers to the use of the principle of laser ranging, by recording the three-dimensional coordinates, reflectivity, texture and other information of a large number of dense points on the surface of the measured object, and quickly reconstructing the three-dimensional model of the measured target and various map data such as lines, surfaces, and bodies.

[0069] The slope geological and geographical data include faults, joints, lithology, veins and other geological and geographical data, which are based on geological data revealed by exploration and mining.

[0070] Specifically, drones equipped with high-definition lenses are used to conduct oblique photogrammetry of the slope during each stage of slope excavation and shaping, slope reinforcement structure construction and completion. Combined with GIS geospatial visualization technology, a three-dimensional transparent geographic impact model is formed based on the collected slope image data.

[0071] In particular, the deep microseismic monitoring module and the ground laser radar are two extremely important parts of the slope monitoring system. In addition to obtaining microseismic data and radar data, the slope monitoring system may only obtain one or several of the above data.

[0072] Specifically, according to the specific structure of the modules in the slope monitoring system, the data collection and transmission between the slope monitoring system and the data cloud platform can be through manual collection and transmission (such as manual pile monitoring data) or through automated means (such as deep displacement data), or a combination of the two.

[0073] Specifically, automatic collection and transmission refers to the way in which the submodules in the slope monitoring system independently complete the collection of monitoring data and transmit it to the data cloud platform. Wireless transmission is suitable for open-pit mine slopes, which can effectively reduce monitoring costs and improve monitoring efficiency.

[0074] The data cloud platform includes a data recording module, a data processing module and a data analysis module. The data recording module is used to store the acquired slope monitoring data and establish a multi-model large database. The data input module is used to manually input the slope manual monitoring data. The data processing module is used to process the stored slope monitoring data. The data analysis module is used to generate the analysis results for the processed slope monitoring data.

[0075] Specifically, the data recording module is used to record and store various types of monitoring data in time series. The time axis is the basic link between various types of monitoring data, and the time relationship is the basis for comprehensive analysis on the same platform. In addition, the original coordinate information in the monitoring data is the basis of the spatial relationship.

[0076] Specifically, the data processing module is used to integrate, correct and process the monitoring data in the data recording module, and finally generate visualization files such as charts and three-dimensional models to form the analysis results of a single monitoring module. At the same time, the monitoring data that exceeds the preset warning value can be fed back to the early warning cloud platform. Among them, the preset warning value is determined by analogy based on local engineering experience and analysis, classification and summary of monitoring data.

[0077] Specifically, the data analysis module includes statistical analysis and multi-source fusion analysis of the monitoring data. The statistical analysis includes time-frequency analysis of microseismic events, automatic identification and analysis of the spatial aggregation morphology of the source points through automatic detection and recognition algorithms, and obtaining spatial aggregation parameters and their spatial relationship with geological structures. The statistical analysis also includes the time-frequency characteristics and spatial characteristics of displacement and water pressure changes, and finally generates visualization files such as charts and three-dimensional models and uploads them to the early warning cloud platform database. The multi-source fusion analysis includes the "time-space-strength" feature fusion analysis of the monitoring data and the three-dimensional analysis of the "point-line-surface-body" fusion in the spatial dimension of displacement data. It uses advanced data fusion and processing algorithms to integrate information from various sensors, and uses deep learning and machine learning technologies for efficient analysis, and predicts the change trend of slope rock in a certain period of time thereafter.

[0078] Specifically, the data analysis module imports slope geological and image data through users, and combines it with the monitoring data processed in the data processing module. It uses advanced algorithms and models to perform slope deformation and stability analysis and verification, and predicts deformation and stability change trends. It also generates analysis results in a timely manner and submits them to the early warning cloud platform.

[0079] Specifically, the analysis results include single monitoring module analysis results and multi-source data fusion analysis results. The multi-source data fusion analysis results are based on data fusion of optional monitoring modules to meet different on-site requirements.

[0080] The early warning cloud platform includes a single monitoring and early warning module, a comprehensive information early warning module, an early warning model optimization module, and a geological image model module. The single monitoring and early warning module is used to provide information feedback based on the analysis results of the single monitoring module, and the comprehensive information early warning module is used to provide information feedback based on the multi-source data fusion analysis results. The feedback results are intuitively displayed in combination with the three-dimensional geological image model.

[0081] Specifically, after receiving the data processed and analyzed by the data cloud platform, the early warning cloud platform can, based on the specific content of the analysis results, issue an alarm for the part of the monitoring data that is abnormal or exceeds the preset warning value and provide real-time feedback to the user. The preset warning value is determined by analogy and analysis, classification and summary of monitoring data based on relevant international standards and local engineering experience.

[0082] Specifically, the early warning model optimization module is used to optimize the control standard of the control platform of the slope monitoring system and the prediction and evaluation method of the slope intelligent monitoring and early warning cloud platform according to the analysis results, and is also used to record and evaluate the monitoring status of each module of the monitoring system to verify or calculate its reliability under special conditions of use. For example, the working status of each module of the monitoring system under severe weather conditions such as heavy rainfall, high temperature in summer, low temperature in winter, and strong winds can be verified.

[0083] The geological image model module is used to intuitively display the three-dimensional transparent slope geological image model formed by the slope image data and geological and geographical data, so as to clearly display the layout of the monitoring equipment and realize the visualization of the monitoring results.

[0084] In a specific embodiment, after receiving the analysis results of the data cloud platform, the early warning cloud platform issues an early warning for the analysis results of abnormal structural stress data and feeds back to the user, so that the user can timely inspect and strengthen the abnormal slope reinforcement structure. Thus, the early warning cloud platform can monitor the use status and effectiveness of the slope reinforcement structure, and issue an accurate early warning according to the status of the slope reinforcement structure, so that the user can adjust and strengthen the slope reinforcement structure in time, and ensure that the slope reinforcement structure is stable as a whole and can effectively support the slope, so as to improve the safety performance of the slope.

[0085] Specifically, with the continuous operation of the slope monitoring and early warning system, the early warning optimization module can comprehensively evaluate the stress and deformation characteristics of the slope through the analysis results sent by the data cloud platform, and optimize and improve the prediction and evaluation methods of the slope intelligent monitoring and early warning cloud platform and the control standards of the control platform. That is, it can intelligently correct and improve the comprehensive evaluation methods and control standards for evaluating slope deformation, stability and quality, analyze and evaluate the preset warning values, and form an intelligent reverse optimization mechanism.

[0086] Specifically, Figure 5 As shown, the slope displacement monitoring method and inclinometer device described in the present invention, the specific process of the present invention is:

[0087] Step 1: Before installing the inclinometer 5-5, embed the GPS positioning device 5-1 into the top monitoring point 5-2 of the inclinometer connecting rod 5-3, i.e., the monitoring zero point. When the inclinometer 5-5 is installed, the monitoring zero point is located on the ground surface, and the GPS positioning device 5-1 automatically obtains the real-time dynamic three-dimensional coordinate information of the monitoring zero point, such as Figure 6 As shown, in the figure, 5-1 is a GPS positioning device, 5-2 is a monitoring point, and 5-3 is an inclinometer connecting rod.

[0088] Step 2: Select the monitored area, plan the layout of the monitoring points 5-2, drill holes in the slope, install the inclinometer 5-5, and the system automatically obtains the initial coordinates D0 (X0, Y0, Z0) of the monitoring zero point, records the distance and burial depth of each monitoring point 5-2, and then calculates the coordinates of other monitoring points 5-2 through the principle of angle displacement. Figure 7 This is the slope profile of the area where the inclinometer is located. 5-4 is the original slope, 5-5 is the inclinometer, and the inclinometer 5-5 is powered by wind and solar energy, and sends monitoring data to the monitoring cloud platform through a wireless transmitter.

[0089] Step 3: When the slope moves, the inclinometer 5-5 moves along with the rock mass. At this time, the inclinometer 5-5 automatically reads the offset angle of each monitoring point 5-2, and uses the top GPS positioning device 5-1 to automatically obtain the real-time coordinate D'0 of the monitoring zero point. Next, the angle displacement principle is used to calculate the actual displacement of the deep part of the slope in the X, Y, and Z directions, such as Figure 8 shown. Figure 8 5-4 is the original slope surface, 5-5 is the initial position of the inclinometer, 5-6 is the position of the inclinometer obtained by the traditional method, 5-7 is the actual position, i.e. the position of the inclinometer obtained by the method of the present invention, and 5-8 is the slope surface after the slope slides. The specific calculation steps are as follows:

[0090] First, according to the initial coordinate D0 of the monitoring zero point, each monitoring point D i Coordinate (X Di , Y Di , Z Di ), when the slope slides, the system automatically obtains the real-time coordinates of the monitoring zero point D'0 (X'0, Y0', Z0'), and uses the real-time coordinates of the monitoring zero point D'0 as the reference point to calculate the real-time coordinates of other monitoring points The coordinates of other monitoring points are calculated according to the following formula:

[0091]

[0092] Among them, L i is the length of the i-th connecting rod (3), L xi For L i Projection on the XZ plane, α xi For L xi The angle with the positive direction of the Z coordinate axis, L yi For L i Projection on the YZ plane, α iy For L yi The angle with the positive direction of the Z coordinate axis, α zi For L i The angle with the positive direction of the Z coordinate axis.

[0093] Next, calculate the displacement of each monitoring point in the X, Y, and Z directions:

[0094]

[0095] Finally, it is concluded that each monitoring point is horizontal and vertical The magnitude of displacement in the direction:

[0096]

[0097] The horizontal and vertical displacements of each inclinometer at the vertical monitoring point 5-2 in the deep part of the landslide reflect the displacement trend in the deep part of the landslide. The system automatically determines the actual displacement and the proposed warning threshold, as shown in Table 1. If the warning threshold is exceeded, the system automatically issues a warning message so that on-site construction personnel can make judgments and evacuate in time.

[0098] Table 1 Warning threshold range

[0099]

[0100] Regarding the effect of the inclinometer, separate embodiments are given, such as Fig.10 The figure shows the profile of a slope in an open pit mine. The slope is 130m high, the horizontal projection is 185m long, and the overall slope is 1:1.42.

[0101] Inclinometer installation position Fig.10 As shown in Table 2, the initial coordinates of the monitoring zero point during installation are D0 (356095.00000, 4616646.91000, 1491.36000). When the slope is displaced, according to the traditional method, it is assumed that the vertex coordinates do not change, the reference point coordinates are D0, and the coordinates and displacements of other monitoring points of the inclinometer are calculated based on D0, as shown in Table 2; however, in actual situations, the vertex displacement will change, and the real-time coordinates of the monitoring zero point D'0 (356095.00720, 4616646.91500, 1491.34874) are automatically obtained by using GPS. With the method of the present invention, the reference point coordinates become D'0 at this time, and the coordinates of other monitoring points of the inclinometer are calculated based on D'0 combined with the angle displacement formula, as shown in Table 2.

[0102] It can be seen from Table 2 that the displacement value obtained by the traditional method is within the safe range, the risk level is none, and the monitoring position is in a safe state; the actual displacement value obtained by the method of the present invention has exceeded the warning threshold, and the risk level is high risk. At this time, the system determines that the actual displacement has exceeded the warning threshold and automatically issues a high-risk level warning information.

[0103] Therefore, by comparison, it is found that: the traditional method, whether it takes the inclinometer vertex as the reference point or the bottom monitoring point as the reference point, can obtain the displacement of other monitoring points, but ignores the displacement change of the reference point. The obtained data cannot reflect the actual change of the deep and surface displacement of the slope and the overall movement trend of the slope, resulting in inaccurate results; while the present invention takes the displacement change of the reference point into consideration, not only can the displacement change of each monitoring point be accurately obtained, but also the system can realize automatic warning according to the proposed warning threshold, thereby ensuring the informatization and automation of mining engineering production.

[0104] Table 2 Comparison of results between traditional method and the method of the present invention

[0105]

[0106] The above is only one of the preferred embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of the present invention.

Claims

1. A cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion, characterized in that: It includes a slope monitoring system, a data cloud platform, a control platform, an early warning cloud platform, and a user terminal; the slope monitoring system is used to monitor the slope rock mass to obtain monitoring data; the data cloud platform is used to obtain the monitoring data and generate independent analysis results based on the monitoring data; The control platform is used to control the data transmission, equipment status, and parameter adjustment of the slope monitoring system; the early warning cloud platform is used to obtain the analysis results, and provide early warning feedback and reasonable suggestions based on the analysis results; The user terminal is used to receive early warning notifications, feedback alarm processing results, conduct two-way interaction between the platform and the site, and realize a closed loop of early warning information feedback.

2. According to claim 1, a cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion is characterized in that: The slope monitoring system includes a deep microseismic monitoring module, a deep displacement monitoring module, a groundwater hydrological monitoring module, a surface displacement monitoring module, a structural stress monitoring module, a blasting vibration monitoring module, an image data acquisition module, and a geological data acquisition module. Data between each module is collected and transmitted independently. The deep microseismic monitoring module adopts an all-fiber microseismic monitoring system, including a digital signal processing system, an acquisition system, an acceleration sensor, an optical cable, a data communication modem and a source positioning software; the digital signal processing system is used to interpret the optical fiber microseismic signal and convert it into a digital signal, the acquisition system is used to collect and store the microseismic signal, the acceleration sensor is used to receive the microseismic signal, the optical cable is used to transmit the microseismic signal received by the acceleration sensor, the data communication modem is used to convert the digital signal into an analog signal for easy transmission, and the source positioning software is used to obtain the source parameters carried by the microseismic waveform; the deep microseismic monitoring module obtains microseismic data, including the coordinates, energy, magnitude and stress drop of the source; The deep displacement monitoring module adopts an inclinometer, and adds a GPS positioning device on the top of the inclinometer to realize settlement monitoring, and adopts a multi-hole joint arrangement to form a three-dimensional monitoring network to obtain deep rock displacement data; The surface displacement monitoring module includes a ground laser radar, a GNSS displacement monitoring station, and an artificial monitoring pile, which are used to obtain slope radar data, GNSS displacement data, and artificial pile monitoring data respectively; The underground hydrological monitoring module includes at least one of the monitoring parameters in the hydrological parameter monitoring, at least a double-hole joint arrangement, and obtains underground hydrological data; The structural stress monitoring module includes at least one of a stress gauge for monitoring stress of an anchor rod, an anchor cable, and a reinforced concrete structure; The blasting vibration monitoring module is used to monitor the particle vibration velocity, using a dedicated three-way vector sensor, and obtain blasting vibration data; The image data acquisition module is used to obtain slope morphological features; The geological data collection module includes the collation of geological exploration and mining exposure data, including a wide range of geological and production data throughout the entire mining cycle.

3. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 1 is characterized in that: The monitoring data, except for manual monitoring data, can be automatically collected and transmitted to the data cloud platform; the monitoring data includes deep microseismic data, deep displacement data, surface displacement data, groundwater hydrological data, stress data of slope support structure, blasting vibration data, slope image data, and slope geological and geographical data; The surface displacement data includes slope radar data, GNSS displacement data, and artificial pile monitoring data; The slope image data includes slope topography data taken by drones, three-dimensional laser scanning data, and oblique image surveying and mapping data; The slope geological and geographical data include fault, joint, lithology, and dyke geological and geographical data revealed by exploration and production.

4. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 1 is characterized in that: The data cloud platform includes a data recording module, a data input module, a data processing module and a data analysis module. The data recording module is used to store the acquired slope monitoring data and establish a multi-model large database. The data input module is used to manually input slope manual monitoring data. The data processing module is used to process the stored slope monitoring data. The data analysis module is used to generate the analysis results for the processed slope monitoring data.

5. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 5 is characterized in that: The multi-model big database refers to the integration of different types of database models into an integrated backend. The database is structured based on time and space elements and serves as the data basis for the calculation of the early warning cloud platform.

6. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 5 is characterized in that: The data processing module is used to correct, integrate and process the original monitoring data in the data recording module, and finally generate a visualization file to form the analysis results of a single monitoring module, which are uploaded to the early warning cloud platform as the basic data of a single monitoring and early warning module, and the analysis results are included in the multi-model large database.

7. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 5 is characterized in that: The data analysis module includes statistical analysis and multi-source fusion analysis of the monitoring data. The statistical analysis includes time-frequency analysis of microseismic events, automatic identification and analysis of the spatial aggregation morphology of the source points through automatic detection and recognition algorithms, and obtaining spatial aggregation parameters and their spatial relationship with geological structures. The statistical analysis also includes time-frequency characteristics and spatial characteristics of displacement and water pressure changes; The multi-source fusion analysis includes the "time-space-strength" feature fusion analysis of the monitoring data and the "point-line-surface-body" fusion three-dimensional analysis of the displacement data in terms of spatial dimension. The data fusion and processing algorithm is used to integrate the information from various sensors, and the deep learning and machine learning technologies are used for analysis, and the change trend of the slope rock in a certain period of time thereafter is predicted; The analysis results include single monitoring module analysis results, statistical analysis results, and multi-source data fusion analysis results. The multi-source data fusion analysis results are based on data fusion of optional monitoring modules to meet different on-site needs.

8. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 1 is characterized in that: The early warning cloud platform includes a single monitoring and early warning module, a comprehensive information early warning module, an early warning model optimization module, and a geological image model module. The single monitoring and early warning module is used to provide information feedback based on the analysis results of the single monitoring module. The comprehensive information early warning module is used to provide information feedback based on the multi-source data fusion analysis results. The feedback results are intuitively displayed in combination with a three-dimensional geological image model. The early warning model optimization module is used to optimize the control standard of the control platform and the prediction and evaluation method of the slope intelligent monitoring and early warning cloud platform according to the analysis results; the geological image model module is used to intuitively display the three-dimensional transparent slope geological image model formed by the slope image data and the slope geological and geographical data, so as to clearly display the layout of the monitoring equipment and realize the visualization of the monitoring results.

9. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 1 is characterized in that: The user terminal is used to receive early warning notifications, feedback alarm processing results, and conduct two-way interaction between the platform and the site. The user terminal will notify the on-site management personnel of the early warning information reviewed by professionals, obtain feedback information after on-site confirmation and upload it to the early warning cloud platform, thus realizing a closed loop of early warning information feedback.

10. The cloud platform for intelligent slope monitoring and early warning based on multi-source data fusion according to claim 2 is characterized in that: The inclinometer comprises a GPS positioning device, a monitoring point and an inclinometer connecting rod; the monitoring points are evenly distributed on the inclinometer connecting rod, the top monitoring point is set as the monitoring zero point, and the GPS positioning device is embedded in the top monitoring point of the inclinometer; The slope displacement monitoring and early warning method comprises the following steps: (1) before the inclinometer is installed, a GPS positioning device is embedded in the top monitoring point of the inclinometer connecting rod, namely, the monitoring zero point; when the inclinometer is installed, the monitoring zero point is located on the ground surface, and the GPS positioning device automatically obtains the real-time dynamic three-dimensional coordinate information of the monitoring zero point. When the monitoring zero point is used as the reference point, the problem that the coordinates of the reference point change in the traditional method causes the data of other monitoring points to be inconsistent with the actual situation is overcome; (2) based on the inclinometer to obtain the measurement data of the deep part of the landslide, these measurement data are converted according to the monitoring zero point, and the displacement in the effective deep X, Y, and Z directions and the accumulated total displacement are automatically obtained, so as to obtain the actual displacement and movement trend of each monitoring point in the deep part of the slope; (3) compared with the proposed early warning threshold, if the early warning threshold is exceeded, the system will automatically issue an early warning message so that the on-site construction personnel can make a judgment and evacuate in time; The step (2) is as follows: selecting the monitored area, planning the arrangement of monitoring points, drilling holes in the slope, installing an inclinometer, and the system automatically obtaining the initial coordinates D0 (X0, Y0, Z0) of the monitoring zero point, recording the distance and burial depth of each monitoring point, and then calculating the coordinates of other monitoring points by the angle displacement principle; when the slope moves, the inclinometer moves along with the rock and soil body, and at this time the inclinometer automatically reads the offset angle of each monitoring point, and automatically obtains the real-time coordinates D'0 of the monitoring zero point by using the GPS positioning device on the top, and then uses the angle displacement principle to calculate the actual displacement of the deep part of the slope body in the X, Y, and Z directions; The specific calculation steps are as follows: First, according to the initial coordinate D0 of the monitoring zero point, each monitoring point D i Coordinate (X Di , Y Di , Z Di ), when the slope slides, the system automatically obtains the real-time coordinates of the monitoring zero point D'0 (X0', Y0', Z'0), and uses the real-time coordinates of the monitoring zero point D'0 as the reference point to calculate the real-time coordinates of other monitoring points The coordinates of other monitoring points are calculated according to the following formula: X' Di =X'0+∑L xi sinα xi AND' Di =Y'0+∑L yi sina yi Z' Di =Z'0-∑L i cosα zi Among them, L i is the length of the i-th connecting rod (3), L xi For L i Projection on the XZ plane, α xi For L xi The angle with the positive direction of the Z coordinate axis, L yi For L i Projection on the YZ plane, α iy For L yi The angle with the positive direction of the Z coordinate axis, α zi For L i The angle with the positive direction of the Z coordinate axis; Next, calculate the displacement of each monitoring point in the X, Y, and Z directions: Finally, it is concluded that each monitoring point is horizontal and vertical The magnitude of displacement in the direction:

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