Intelligent landslide displacement monitoring and early warning system and method based on multi-source data

Abstract

The invention discloses an intelligent landslide displacement monitoring and early warning system and method based on multi-source data, and the system comprises a plurality of landslide monitoring devices disposed on a landslide body, and a landslide monitoring platform in communication connection with the landslide monitoring devices. GNSS satellite high-precision monitoring, underground seepage field monitoring, underground stress field monitoring, meteorological environment monitoring and multi-source data intelligent analysis and forecasting are organically combined, so that the advantages are complementary, and an overground-underground three-dimensional monitoring technology system and an intelligent data analysis platform are formed; the problems of incomplete landslide disaster monitoring, poor data linkage and backward data analysis means in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide monitoring and early warning, and in particular to an intelligent landslide displacement monitoring and early warning system and method based on multi-source data. Background Art

[0002] Landslide disasters are natural phenomena caused by geological, rainfall, earthquake and other factors, in which the surface soil or rock slides down the slope. They often occur in high-risk slopes and areas with complex geological conditions. Landslide disasters not only damage the ecological environment, but may also cause serious property losses and casualties, especially around important infrastructure such as transportation and construction, which poses a great threat. In order to reduce the harm caused by landslides, high-precision monitoring and early warning forecasts are essential. Through accurate monitoring and intelligent analysis, landslide precursors can be captured in time, providing valuable time for relevant departments to take measures. However, existing landslide monitoring devices have problems such as fewer monitoring items, single data analysis methods, and delayed response time, resulting in unsatisfactory monitoring effects. How to effectively obtain multi-source slope monitoring data at low cost, and use efficient data analysis methods to achieve high-precision monitoring and early warning forecasts of landslides is a challenge that needs to be solved urgently.

[0003] In the current research on early warning and forecasting of landslide disasters, ground sensors or non-contact sensors are usually used, and then the sensor values ​​are analyzed to determine the development status of the landslide. Among them, monitoring equipment that measures the change in landslide displacement is more commonly used, and common ones include displacement meters that measure regional deformation, and GNSS technology that measures deformation within a certain range. However, the use of displacement meters has the problem of too small a monitoring range and low measurement accuracy. Although GNSS technology can characterize the displacement of the slope, a single displacement monitoring data cannot fully characterize the changes in the landslide. Common non-contact sensors include industrial cameras, laser scanners, etc. These instruments can intuitively display the on-site conditions of the landslide, but cannot provide timely warnings. The existing landslide early warning methods are mainly judged by analyzing the trend of the landslide deformation, and cannot accurately predict landslides during the mutation period.

[0004] Based on the above-mentioned current characteristics of landslide monitoring, early warning and forecasting equipment, the current landslide monitoring has the following problems: the accuracy of landslide displacement monitoring is not high; the monitoring means are single, the monitoring data source is not comprehensive enough and the data linkage is poor; the data analysis means are backward, the data interpretation method is primitive, the early warning has a certain lag, and the forecast effect is poor. Summary of the invention

[0005] The present application provides an intelligent landslide displacement monitoring and early warning method and system based on multi-source data to solve the problems of incomplete landslide disaster monitoring, poor data linkage and backward data analysis methods in the prior art.

[0006] According to the first aspect, an embodiment provides an intelligent landslide displacement monitoring and early warning system based on multi-source data, the system comprising a plurality of landslide monitoring devices deployed on a landslide body and a landslide monitoring platform in communication connection with the landslide monitoring devices;

[0007] The landslide monitoring equipment is used to monitor the landslide displacement, meteorological environment and physical properties of the landslide body, and upload the monitoring data to the landslide monitoring platform in real time;

[0008] The landslide monitoring equipment includes a ground monitoring part and an underground monitoring part. The ground monitoring part is used to monitor the displacement and meteorological environment status of the landslide area around the clock, and the underground monitoring part is used to monitor the changes in the seepage field and stress field inside the landslide body.

[0009] The landslide monitoring platform is used for landslide monitoring equipment management, monitoring task management and monitoring data management. It comprehensively processes the multi-source monitoring data obtained by the landslide monitoring equipment to achieve comprehensive monitoring, analysis and prediction of the landslide body, and provide support for landslide risk assessment and emergency management.

[0010] Furthermore, the ground monitoring part includes a GNSS monitoring station and a micro-meteorological station;

[0011] The GNSS monitoring station is used to monitor the displacement changes of the landslide body in real time and obtain three-dimensional displacement data with millimeter-level accuracy through differential calculation;

[0012] The micro-meteorological station is used to monitor the meteorological environment data around the landslide body.

[0013] Further, the underground monitoring part includes a pore water pressure gauge and a strain gauge;

[0014] The pore water pressure gauge is used to monitor the changes in pore water pressure inside the landslide body and evaluate the seepage field of the landslide body;

[0015] The strain gauge is used to monitor the stress change inside the landslide body and evaluate the stability of the landslide body.

[0016] Furthermore, the landslide monitoring device also includes a data communication unit and a power management unit;

[0017] The data communication unit is used to transmit the collected data to the landslide monitoring platform and receive the configuration instructions and alarm information issued by the platform;

[0018] The power management unit is used to support solar power supply and battery power supply to ensure continuous power supply of the device and has a wireless charging function.

[0019] Furthermore, the landslide monitoring platform specifically includes:

[0020] A data receiving and storage unit, used for receiving and storing monitoring data of landslide monitoring equipment via a wireless or wired network, and supporting automatic data backup and recovery;

[0021] The data processing and editing unit is used to automatically process and correct the monitoring data, automatically identify and eliminate abnormal data, use deep learning algorithms to supplement and predict missing values ​​and data change trends, and visualize the changes in monitoring data of the landslide body, supporting users to expand and fill in monitoring reports.

[0022] Furthermore, the landslide monitoring platform specifically includes:

[0023] The trend analysis unit is used to use historical monitoring data, based on physical models or machine learning algorithms, to evaluate the activity of landslide bodies, predict the time and scale of landslide instability, and estimate the future development trend of landslides; based on finite element simulation experiments on the landslide body area, the critical stress parameters of landslide stability are obtained as warning values, and by monitoring the changes in underground stress values ​​and analyzing the trends of monitoring data, it helps to warn and forecast landslide disasters;

[0024] The alarm unit is used to issue an alarm when the monitoring data reaches the set landslide instability threshold and send the alarm information to the relevant management department through wireless communication.

[0025] Furthermore, the landslide monitoring platform specifically includes:

[0026] The user operation interactive interface is used to provide real-time monitoring data display, allowing users to view landslide monitoring data and equipment status, and supports multiple operations including remote equipment configuration, monitoring frequency adjustment, and alarm parameter setting.

[0027] Furthermore, the landslide monitoring platform specifically includes:

[0028] The emergency response support unit is used to work with the local emergency management system. When a landslide warning is triggered, the emergency response mechanism is activated, and disaster loss assessment is conducted in combination with historical monitoring data to help quickly formulate emergency plans.

[0029] According to the second aspect, an embodiment provides a method for implementing an intelligent landslide displacement monitoring and early warning system based on multi-source data, the method comprising:

[0030] Determine the monitoring area: select the approximate range of the slopes based on the high-risk slopes to be monitored, and further refine the specific areas and boundaries that need to be monitored based on various geological factors on site;

[0031] Obtain basic data of the monitoring area: obtain various basic data of the monitoring area through geological survey, and determine the critical values ​​of landslide parameters when the landslide body slides through finite element simulation;

[0032] Build a landslide monitoring platform: select computing servers, databases, and necessary network equipment to ensure the reliability of data processing and storage; install and configure the landslide monitoring platform software to ensure real-time data reception, processing, analysis, visualization, and alarm functions; establish a preliminary landslide prediction model based on regional characteristics and basic data, and integrate machine learning algorithms or physical models to predict landslide behavior;

[0033] Arrange and install monitoring equipment: determine the installation locations of the ground monitoring part and the underground monitoring part related equipment; finally complete the installation of the data communication unit, connect the monitoring equipment data to the landslide monitoring platform, and conduct preliminary calibration and debugging to ensure accurate data transmission;

[0034] Automatically complete monitoring tasks: The monitoring frequency and time of each device can be set through the landslide monitoring platform, and the monitoring task can be performed around the clock without human intervention; the landslide monitoring equipment automatically collects data on landslide displacement, meteorological environment and physical properties of the landslide body, and uploads them to the landslide monitoring platform in real time; the landslide monitoring platform automatically classifies and stores the uploaded data, and establishes a historical record of landslide monitoring data;

[0035] Monitoring data analysis and processing: Through the automated data processing of the landslide monitoring platform, abnormal data is filtered, sensor errors are corrected, and data accuracy is ensured; the trend analysis function of historical data is used to identify changes in the displacement acceleration of the landslide body and assess the activity of the landslide; combined with multi-source data, machine learning or physical models are used to predict the future behavior of the landslide, focusing on the acceleration trend of the landslide, and predicting the possible time and scale of instability; when the landslide monitoring data reaches the preset instability threshold, an alarm is automatically issued, and the relevant emergency management department is notified to initiate an emergency response;

[0036] Subsequent maintenance of monitoring equipment: arrange professional personnel to check the equipment regularly to ensure that the equipment is working properly; automatically issue a fault alarm when a failure occurs in the equipment or platform, and arrange personnel to repair and replace it in time; regularly check the power supply system to ensure that the monitoring equipment always has sufficient power supply.

[0037] According to the third aspect, an embodiment provides an early warning method of an intelligent landslide displacement monitoring and early warning system based on multi-source data, the method comprising:

[0038] Conduct finite element simulation experiments on the landslide area to obtain the critical values ​​of soil physical parameters under the critical instability conditions of specific landslides as warning values;

[0039] During the operation of the landslide monitoring equipment, monitoring data is obtained through the ground monitoring part and the underground monitoring part. The ground monitoring part obtains the displacement change data of the landslide body, and realizes the prediction of the landslide body based on the displacement change characteristics of the creep stage, sliding stage, and severe sliding stage of the landslide body deformation and combined with the deep learning prediction algorithm; the underground monitoring part realizes the stability judgment of the landslide body by real-time monitoring of the soil physical parameters of the landslide body and comparing them with the critical values ​​of the soil physical parameters under critical instability conditions;

[0040] When the displacement change rate of the landslide body or one of the soil physical parameters approaches the critical value, a yellow alarm is triggered. The landslide monitoring platform sends a yellow alarm message and activates the corresponding response mechanism to increase the frequency of landslide monitoring. When the displacement change rate of the landslide body and the soil physical parameters approach the critical value at the same time, a red alarm is triggered. The landslide monitoring platform issues a red alarm message and activates the emergency response plan to reduce the losses caused by landslide disasters.

[0041] The present application provides an intelligent landslide displacement monitoring and early warning method and system based on multi-source data, comprising a plurality of landslide monitoring devices deployed on a landslide body and a landslide monitoring platform in communication with the landslide monitoring devices; the landslide monitoring devices are used to monitor the landslide displacement, meteorological environment and physical properties of the landslide body, and upload the monitoring data to the landslide monitoring platform in real time; the landslide monitoring equipment comprises a ground monitoring part and an underground monitoring part, the ground monitoring part is used to perform all-weather displacement and meteorological environment status monitoring of the landslide area, and the underground monitoring part is used to monitor the changes in the seepage field and stress field inside the landslide body; the landslide monitoring platform is used for landslide monitoring equipment management, monitoring task management and monitoring data management, and comprehensively processes the multi-source monitoring data obtained by the landslide monitoring equipment, realizes comprehensive monitoring, analysis and prediction of the landslide body, and provides support for landslide risk assessment and emergency management. The organic combination of GNSS satellite high-precision monitoring, underground seepage field monitoring, ground stress field monitoring, meteorological environment monitoring, and multi-source data intelligent analysis and forecasting complements each other's advantages, forming a "ground-underground" three-dimensional monitoring technology system and an intelligent data analysis platform, which solves the problems of incomplete landslide disaster monitoring, poor data linkage, and backward data analysis methods in existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of the structure of an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention;

[0043] Figure 2 A structural diagram of a landslide monitoring device in an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention;

[0044] Figure 3A schematic diagram of the layout of an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of a slope displacement monitoring platform in an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention;

[0046] Figure 5 A landslide development displacement curve diagram in an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention;

[0047] Figure 6 A flow chart of finite element analysis calculation of landslide body in an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by one embodiment of the present invention;

[0048] Figure 7 The present invention provides an intelligent landslide displacement monitoring and early warning system based on multi-source data in an embodiment of the present invention, which provides the displacement and rate changes of the landslide body under different physical parameter conditions;

[0049] Figure 8 An embodiment of the present invention provides an estimation of landslide volume under different working conditions in an intelligent landslide displacement monitoring and early warning system based on multi-source data;

[0050] Fig. 9 A schematic diagram of the logical structure of an intelligent landslide displacement monitoring and early warning system based on multi-source data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0052] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0053] The first embodiment of the present invention provides an intelligent landslide displacement monitoring and early warning system based on multi-source data, the system includes a plurality of landslide monitoring devices 08 deployed on the landslide body and a landslide monitoring platform 01 in communication with the landslide monitoring devices 08. Figure 1 Provide detailed explanation.

[0054] The landslide monitoring equipment 08 is used to monitor the landslide displacement, meteorological environment and physical properties of the landslide body, and upload the monitoring data to the landslide monitoring platform 01 in real time. Figure 3 shown.

[0055] The landslide monitoring equipment 08 includes a ground monitoring part 15 and an underground monitoring part 18. The ground monitoring part 15 is used to monitor the displacement and meteorological environment status of the landslide area around the clock, and the underground monitoring part 18 is used to monitor the changes in the seepage field and stress field inside the landslide body. Figure 2 As shown, during the installation process, the ground monitoring part 15 is located on the pole above the cement foundation, and the underground monitoring part 18 passes through the cement foundation and is deployed underground at the side of the foundation.

[0056] The monitoring unit of the embodiment of the present invention is divided into two parts: ground displacement monitoring and underground stress monitoring. The ground displacement monitoring mainly realizes landslide warning by analyzing the displacement data of the landslide body in combination with advanced prediction algorithms; the underground stress monitoring is based on the underground monitoring data, and the various stress parameters of the landslide body in the critical instability state are obtained as warning values ​​through finite element simulation technology, and the landslide warning function is realized by real-time monitoring of the stress changes of the landslide body.

[0057] Specifically, the ground monitoring part 15 in this embodiment is used to automatically collect the displacement of the landslide surface and the meteorological factors closely related to the landslide activity around the clock. The unit obtains the surface changes and environmental information of the landslide in real time by combining high-precision GNSS monitoring and micro-meteorological stations, and transmits the data to the monitoring platform for analysis and processing.

[0058] In this embodiment, the ground monitoring part 15 includes a GNSS monitoring station 16 and a micro-meteorological station 17 .

[0059] The GNSS monitoring station 16 is used to monitor the displacement changes of the landslide in real time and obtain three-dimensional displacement data with millimeter-level accuracy through differential calculation. Specifically, it includes a GNSS antenna and a GNSS receiver; the GNSS antenna can receive signals from multiple navigation satellites (such as GPS, Beidou, GLONASS, etc.), and the antenna has an anti-interference function, which can effectively resist electromagnetic interference in the natural environment; the GNSS receiver processes the satellite signals received by the antenna, uses differential technology (RTK or PPP) for precise calculation, and obtains the change in the three-dimensional coordinates of the landslide; the receiver has high-precision calculation capabilities, and the displacement monitoring accuracy can reach the millimeter level.

[0060] In this embodiment, the GNSS receiver's high-precision static positioning has a horizontal accuracy of approximately 3mm+0.1ppm RMS and a vertical accuracy of approximately 3.5mm+0.4ppm RMS. The receiver supports L1, L2, L5 and other operating frequency bands, and the data update frequency is 1Hz-100Hz. The power consumption is within 6W, and the data communication interfaces include RS232, Ethernet, Wi-Fi, etc. It supports multiple transmission protocols such as TCP / IP and NTRIP, and can work normally in an environment of -40°C to +65°C, with a protection level of IP68.

[0061] Micro-weather station 17 is used to monitor the meteorological environment data around the landslide body. Specifically, it includes temperature and humidity sensors, precipitation sensors, and air pressure sensors. Temperature changes may cause thermal expansion and contraction inside the landslide body, while humidity changes, especially changes in moisture content in the air, are closely related to soil moisture content and affect slope stability. Precipitation is an important factor in inducing landslides. Precipitation sensors can monitor precipitation in real time and compare and analyze it with historical rainfall data to evaluate the rising trend of soil moisture content and provide a basis for landslide risk warning. Air pressure changes are closely related to weather systems, especially in atmospheric low-pressure systems, which may be accompanied by heavy rainfall or storms. Air pressure sensors provide support for meteorological trend prediction by recording air pressure fluctuations.

[0062] In this embodiment, the temperature measurement range of the micro weather station is -40°C to +100°C, with an accuracy of ±0.4°C; the humidity measurement range is 0% to 99.9%, with an accuracy of ±3%; the air pressure measurement range is 300hPa to 1100hPa, with an accuracy of ±0.5hPa; the precipitation measurement range is 0 to 4mm / min, with an accuracy of ±0.4mm.

[0063] In this embodiment, the underground monitoring part 18 is used to obtain monitoring data of underground seepage field and ground stress field. The changes of seepage field and stress field are often closely related to landslide activities. The combination of the two can more comprehensively reflect the internal conditions of the landslide body. Through the linkage monitoring of underground seepage and stress changes, the accuracy and timeliness of landslide warning can be improved, and the loss of personnel and property when disasters occur can be reduced. The connection with the ground control unit is achieved through an optical cable; the internal physical parameters of the landslide body are obtained, and the stability state of the landslide body is judged by finite element analysis.

[0064] In this embodiment, the underground monitoring part 18 includes a strain gauge 19 and a pore water pressure gauge 20 .

[0065] The strain gauge 19 is used to monitor stress changes inside the landslide body and evaluate the stability of the landslide body. Specifically, the strain gauge 19 is used to measure the deformation and stress state of the rock mass or soil. It is usually buried inside the landslide body, usually arranged at different depths and orientations along the landslide body to capture deformation and stress changes caused by geological stress, external loads or landslide movement; the strain gauge can monitor and record changes in landslide body stress in real time, and the data combined with surface displacement monitoring data for analysis will help to more comprehensively evaluate the dynamic trend of the landslide. It includes a measuring probe, a base material, and a cable; the measuring probe is used to measure the rock formation pressure value, the base material is used to protect the measuring probe, and the measurement and data signal transmission are achieved through the cable.

[0066] In this embodiment, the tensile and compressive ranges of the strain gauge are both greater than or equal to 1500 με, the operating temperature is -20°C to 80°C, the resolution is 0.016% FS, and the built-in data storage prevents data transmission interruption.

[0067] The pore water pressure gauge 20 is used to monitor the changes in pore water pressure inside the landslide body and evaluate the seepage field of the landslide body. The pore water pressure gauge 20 is used to measure groundwater pressure, also known as pore water pressure, and can provide information on the dynamic changes of groundwater. It is usually installed in a borehole to monitor changes in pore water pressure, especially in the rainy season or when the groundwater level rises, to record the dynamic changes of groundwater; the pore water pressure gauge has high measurement accuracy and can record subtle changes in groundwater pressure, helping to promptly detect the potential impact of water infiltration on the landslide body. Specifically, it includes a pressure sensor, a filter head, a protective tube, a cable, and a temperature sensor; the pressure sensor is used to measure pore water pressure, the filter head filters soil particles, the protective tube protects the internal sensor and cable, and the cable transmits sensor signals and power supply.

[0068] In this embodiment, the pore water pressure meter has a range of 0-0.6MPa, a resolution of 0.1Kpa, an accuracy of ±0.1%FS, a diameter of 25mm, a length of 170mm, an operating ambient temperature of -20°C to 80°C, and built-in data storage to prevent data transmission interruption.

[0069] In this embodiment, the landslide monitoring device 08 further includes a data communication unit 09 and a power management unit 10;

[0070] The data communication unit 09 is used to transmit the collected data to the landslide monitoring platform 01, and receive the configuration instructions and alarm information issued by the platform. For ground monitoring data, the landslide displacement monitoring data, meteorological factor data and equipment power status data after precise calculation are transmitted to the landslide monitoring platform. Communication can be carried out through wireless transmission (such as 4G, LoRa or Wi-Fi, etc.) to ensure the real-time upload of remote data; the equipment can receive instructions such as monitoring parameter configuration, frequency adjustment, alarm setting, etc. issued by the monitoring platform to achieve remote management; it supports two-way communication, which can transmit data and receive commands to ensure the flexibility and real-time performance of the system; it has local storage function. If the communication is temporarily interrupted, it can store monitoring data for a certain period of time to ensure that the data is not lost after the communication is restored. For underground monitoring data, it is responsible for the real-time transmission of underground monitoring data to ensure that the monitoring data can be uploaded to the landslide monitoring platform in time for data analysis and early warning.

[0071] The power management unit 10 is used to support solar power supply and battery power supply, ensure the continuous power supply of the device, and have a wireless charging function. Specifically, it includes a voltage stabilizing module, a battery module, a solar panel, and a wireless charging transmitter module. The voltage stabilizing module ensures that each sensor and communication module can operate stably under different voltage environments and protects the device from voltage fluctuations; it is equipped with a large-capacity rechargeable battery to ensure the continuous operation of the device in harsh climates or at night when there is no sunlight. The battery life is designed to be at least several days to ensure power supply stability; the solar panel is responsible for converting solar energy into electrical energy, powering the device during the day and charging the battery, extending the operating cycle of the device, especially suitable for remote areas and monitoring sites without power supply; it has a wireless charging function, which is convenient for maintenance personnel to replenish power through a portable charging device without disassembling the equipment, reducing downtime.

[0072] In this embodiment, the battery module uses a 24V lithium battery with a total capacity of not less than 45Ah, the solar panel area is not less than 1.5m2, the output power is not less than 150W, and the wireless charging transmission modules of the ground monitoring system all use high-power coils.

[0073] Landslide Monitoring Platform 01 is used for landslide monitoring equipment management, monitoring task management and monitoring data management. It comprehensively processes the multi-source monitoring data obtained by the landslide monitoring equipment, realizes comprehensive monitoring, analysis and prediction of the landslide body, and provides support for landslide risk assessment and emergency management.

[0074] In this embodiment, the landslide monitoring platform 01 specifically includes:

[0075] The data receiving and storage unit 02 is used to receive and store the monitoring data of the landslide monitoring device 08 through a wireless or wired network, and supports automatic data backup and recovery. Specifically, it is used to receive and store the data collected by the landslide monitoring equipment, including monitoring information such as landslide displacement, groundwater level, stress change, and meteorological environment; through wireless communication (such as 4G, LoRa, NB-IoT, etc.) or wired network, the platform receives the monitoring data uploaded by each sensor in real time, establishes a reliable data storage mechanism, and ensures the safe preservation of long-term data. Supports automatic backup and recovery to prevent data loss due to system failure.

[0076] The data processing and editing unit 06 is used to automatically process and correct the monitoring data, automatically identify and eliminate abnormal data, use deep learning algorithms to supplement and predict missing values ​​and data change trends, and visualize the changes in the monitoring data of the landslide body, supporting users to expand and fill in monitoring reports.

[0077] In this embodiment, the data processing and editing unit 06 specifically uses the long short-term memory network algorithm to supplement and predict the missing values ​​and the change trend of the data, so as to ensure the accuracy of the data and reduce false positives and false negatives. If the time series of the previous moment of the missing value is represented by x t , the missing values ​​can be calculated using the following formula:

[0078] f t =f sig (W hf h t-1 +U xf x t +B f )

[0079] i t =f sig (W hi h t-1 +U xi x t +B i )

[0080] v t =tanh(W hc h t-1 +U xc x t +B c )

[0081] o t =f sig (W ho h t-1 +U xo x t +B o )

[0082] The long short-term memory network is mainly composed of three parts: forget gate, input gate and output gate. In the above formula, f t 、i t , o t are the output results of the forget gate, input gate and output gate at time t respectively; v t is the information input at time t; h t-1 Indicates the hidden state of the previous moment; x t is the information of the landslide time series at the current time step; f sig represents the Sigmoid activation function; tanh is the hyperbolic tangent activation function; W hf , W hi , W ho They are respectively the forget gate, input gate, and output gate corresponding to h t-1 The weight matrix of hc h t-1 and x t The connection weight matrix, U xf , U xi , U xo They are respectively the forget gate, input gate, and output gate corresponding to x t The weight matrix of xc v t The connection weight matrix of B f , B i , B o are the bias vectors of the forget gate, input gate, and output gate respectively; B c is the bias vector of the new information. After calculation, t This is the missing value data obtained.

[0083] In this embodiment, the data processing and editing unit 06 can automatically identify abnormal data. The calculation process assumes that the change of the landslide displacement before and after the period is relatively continuous. Therefore, the time series data is subtracted before and after to obtain a set of rate sequences about the time series. The isolated forest algorithm is then used to detect abnormal values ​​for this set of rate sequences, and the abnormal value positions are marked and the abnormal values ​​at the corresponding positions are removed; the Kalman filter algorithm is used to predict and supplement the time series positions after removal. The Kalman filter algorithm can be expressed by the following formula:

[0084]

[0085]

[0086] The first two formulas are the system state prediction part, and the last three formulas are the system correction part. The most important thing about the whole system is to express the relationship between the system model and the measurement and state through the formula:

[0087] x k+1 =Ax k +Bu k +w k

[0088] z k =Hx k +v k

[0089] Where A is the system matrix, B is the control matrix, and H is the observation matrix. k is the state of the target at time k, u is the control variable, ω and v are the system error and measurement error respectively.

[0090] In addition, by obtaining the meteorological data of the monitoring station in real time and correcting the model parameters through the environmental correction model, it ensures that the deviations caused by environmental changes or equipment errors are reasonably adjusted, providing accurate displacement and stress monitoring data. The unit has powerful visualization capabilities and can display real-time monitoring data such as displacement, groundwater level, and stress changes of the landslide body in the form of charts, maps, and three-dimensional models, helping users quickly understand the dynamic changes of the landslide body, such as Figure 4 The figure shows a schematic diagram of the slope displacement monitoring platform. The monitoring data of each sensor is displayed on the right side, and the bottom right side shows the development of the landslide.

[0091] It can also automatically generate monitoring reports based on monitoring data, and support users to fill in extended data, helping managers to record and analyze detailed data.

[0092] Trend analysis unit 05 is used to use historical monitoring data, based on physical models or machine learning algorithms, to evaluate the activity of landslide bodies, predict the time and scale of landslide instability, and estimate the future development trend of landslides; based on finite element simulation experiments on the landslide body area, the critical stress parameters of landslide stability are obtained as warning values, and by monitoring the changes in underground stress values ​​and analyzing the trends of monitoring data, it helps to warn and predict landslide disasters.

[0093] Specifically, using historical monitoring data, the system can identify the acceleration changes in the displacement of the landslide body, assess the activity of the landslide body, and predict future risks, such as Figure 5 The development of the landslide body shown generally goes through three stages. The daily acceleration change of the landslide body can be calculated through historical data. If the movement speed of the landslide body is greater than 5mm for three consecutive days, an early warning is required.

[0094] Based on physical models or machine learning algorithms, the platform can predict the future development trend of landslides. Physical models mainly refer to Figure 6The landslide development displacement curve shown in the figure determines the development status of the landslide body by analyzing the historical monitoring data. The deep learning algorithm mentioned above can also be used for landslide displacement prediction to help analyze the changes in landslide displacement, especially when the landslide displacement accelerates. The system will give an estimate of the time and scale of the possible landslide, providing decision support for emergency management. When deploying, this device needs to conduct finite element simulation experiments on the deployment area to obtain the critical stress parameters of the stability of the landslide in the deployment area as the warning value. By monitoring the changes in underground stress values ​​and analyzing the trends of monitoring data, it helps to warn and forecast landslide disasters.

[0095] Finite element simulation experiments on landslides are mainly implemented using the finite element difference method. In the solution process, the continuous problem needs to be discretized first, that is, the solution area is meshed. The calculation steps are as follows: Figure 7 As shown:

[0096] During the numerical simulation process, each stratum is defined as a continuously distributed isotropic medium, and the Moore-Coulomb constitutive model is used for calculation; the bottom of the model is set as a fixed boundary, the four sides of the model are vertical boundaries, and the slope surface is set as a free boundary. Based on the high-precision soil stress monitoring data, soil moisture and groundwater level information fine model in the landslide area, an accurate three-dimensional model of the landslide with geological information is obtained; the stress data, soil moisture and other data of the slope soil are used as constraints to calculate whether the shear strain increment zone and the plastic zone of the landslide are connected as the criterion for landslide instability, and then the parameters of the constraint conditions are changed until the numerical simulation results show an unstable state, and the stress data, soil moisture and other parameter values ​​of the slope soil in the unstable state are used as warning values. As follows Figure 4 It is a cloud diagram of displacement and velocity of the landslide body under different parameters.

[0097] During the development of landslides, rainfall factors of different scales will induce landslides to have different scales of impact. These environmental factors will be reflected in the changes in soil physical parameters. When landslides occur under different soil physical parameters, the volume of the landslide body is also different. By simulating the volume of landslides under different working conditions, it can assist in disaster relief work, such as Figure 8 Shown are the results of simulated landslide scale under different soil physical parameters.

[0098] The alarm unit 04 is used to issue an alarm when the monitoring data reaches the set landslide instability threshold, and send the alarm information to the relevant management department through wireless communication. By converting the operating status of each monitoring device and the monitoring data analysis results of the sensor into an alarm signal, when the monitoring data reaches the set landslide instability threshold, the system will issue an alarm; including equipment power alarm, equipment signal alarm, anti-theft alarm, equipment failure alarm, and landslide danger abnormal movement alarm.

[0099] User operation interactive interface 03 is used to provide real-time monitoring data display, allowing users to view landslide monitoring data and equipment status, and supports multiple operations including remote equipment configuration, monitoring frequency adjustment, and alarm parameter setting. Specifically, it can provide an intuitive and friendly operation interface for users to monitor landslide conditions in real time and configure and manage equipment; the interface displays the relative changes in landslide displacement, meteorological environment (such as precipitation, wind speed, temperature and humidity, etc.), underground seepage field and stress field monitoring data. Users can view data changes at different monitoring points at any time to understand the dynamic trend of the landslide body; they can remotely set and manage monitoring equipment parameters, manually adjust monitoring frequency, perform equipment maintenance, view equipment operating status, and other operations.

[0100] Emergency response support unit 07 is used to link with the local emergency management system. When the landslide warning is triggered, the emergency response mechanism is activated, and the disaster loss assessment is carried out in combination with historical monitoring data to help quickly formulate emergency plans. By linking with the local emergency management system, the landslide warning is automatically triggered and the emergency response mechanism is quickly activated to help reduce the loss of life and property caused by the landslide; when the landslide warning is triggered, the system automatically sends an alarm message to the local emergency management department and activates the corresponding emergency response plan; after the landslide occurs, the platform can combine historical monitoring data and information on the affected area to provide a preliminary assessment of the disaster loss, help emergency management personnel quickly understand the scope and extent of the disaster, and formulate response measures; support the creation and drills of emergency plans, and help relevant departments regularly test and optimize emergency response capabilities.

[0101] Corresponding to the above-disclosed intelligent landslide displacement monitoring and early warning system based on multi-source data, the embodiment of the present invention further discloses a method for implementing the intelligent landslide displacement monitoring and early warning system based on multi-source data, such as Fig. 9 As shown, it specifically includes the following steps:

[0102] S110, determine the monitoring area: select the approximate slope range based on the high-risk slopes to be monitored, and further refine the specific areas and boundaries to be monitored based on factors such as slope, soil type, and geological structure;

[0103] In this step, the landslide range that needs to be monitored, in addition to the high-risk slopes, must also include a portion of stable open space for placing GNSS base stations. The location selection of the monitoring points must fully consider the satellite signals, and the prone areas must focus on deploying more equipment.

[0104] S120, obtaining basic data of the monitoring area: obtaining basic data such as rock type, soil thickness, groundwater level distribution, etc. of the monitoring area through geological survey, and determining critical values ​​of landslide parameters when the landslide body is sliding through finite element simulation method;

[0105] In this step, the unstable parameters of the landslide body are obtained by finite element simulation, and are reflected in the internal parameter values ​​of the landslide body. The parameter values ​​are entered into the landslide monitoring platform as early warning parameters to assist in landslide disaster early warning and forecasting.

[0106] S130, build a landslide monitoring platform: select computing servers, databases and necessary network equipment to ensure the reliability of data processing and storage; install and configure the landslide monitoring platform software to ensure that it has real-time data reception, processing, analysis, visualization and alarm functions; establish a preliminary landslide prediction model based on regional characteristics and basic data, and integrate machine learning algorithms or physical models to predict landslide behavior;

[0107] In this step, the selection of computing servers should be targeted, and the number of monitoring devices under different monitoring situations should be considered to ensure smooth and efficient data transmission and data calculation.

[0108] S140, arrange and install monitoring equipment: select the installation location of GNSS monitoring station, seepage meter, pore water pressure meter and other equipment according to factors such as regional slope, seepage path, geological stress distribution, etc.; install GNSS monitoring station and micro-meteorological station on the cement base to ensure that the equipment is stable and has no obstructions above, and can collect data around the clock; arrange pore water pressure meter and stress meter diagonally below the cement base to ensure that the installation depth and direction of the equipment meet the monitoring requirements; finally, complete the installation of data communication module, connect the equipment data to the monitoring platform, and perform preliminary calibration and debugging to ensure accurate data transmission.

[0109] In this step, the equipment layout and installation diagram is as follows Figure 3 As shown, the installation of monitoring equipment needs to fully consider the geological conditions to ensure that the monitoring station will not collapse. The layout must consider the continuity of satellite signal reception, and the layout location must also be able to reflect the overall landslide changes.

[0110] S150, automatically complete the monitoring task: the monitoring frequency and time of each device are set through the monitoring platform, and the monitoring task can be performed around the clock without human intervention; the monitoring equipment automatically collects the displacement, seepage, stress and meteorological data of the landslide body, and uploads them to the monitoring platform in real time; the monitoring platform automatically classifies and stores the uploaded data, and establishes a historical record of the landslide monitoring data;

[0111] In this step, when the monitoring platform is debugged, it enters the monitoring state. Under normal circumstances, the monitoring station should be in a 24-hour monitoring state. When force majeure causes transmission interruption, manual copying of monitoring data is required on site.

[0112] S160, monitoring data analysis and processing: Through the platform's automated data processing module, filter abnormal data, correct sensor errors, and ensure data accuracy; use the trend analysis function of historical data to identify changes in the acceleration of landslide displacement and evaluate the activity of the landslide; combine multi-source data and use machine learning or physical models to predict the future behavior of the landslide, focusing on the acceleration trend of the landslide and predicting the possible time and scale of instability; when the landslide monitoring data reaches the preset instability threshold, the system automatically issues an alarm and notifies the relevant emergency management department to initiate an emergency response.

[0113] In this step, the selection of analysis algorithms for monitoring data should be targeted. A more suitable analysis algorithm should be selected for different types of landslides. At the same time, missing data can be supplemented when data monitoring is interrupted.

[0114] S170, Subsequent maintenance of monitoring equipment: arrange professionals to regularly check the equipment, including GNSS antennas, weather stations, seepage meters, pressure gauges, etc., to ensure their normal operation; when equipment or platforms fail, the system will automatically issue a fault alarm and arrange personnel to repair and replace them in time; regularly check the power supply system, especially the solar power supply device, to ensure that the monitoring equipment always has sufficient power supply.

[0115] In this step, the stability of the equipment is mainly judged through the monitoring data of the monitoring platform. When data is missing or abnormal, troubleshooting is carried out through on-site inspections.

[0116] Corresponding to the above-disclosed intelligent landslide displacement monitoring and early warning system based on multi-source data, the embodiment of the present invention further discloses an early warning method of an intelligent landslide displacement monitoring and early warning system based on multi-source data, which specifically includes the following steps:

[0117] S210, conducting a finite element simulation experiment on the landslide area to obtain critical values ​​of soil physical parameters under critical instability conditions of a specific landslide as early warning values;

[0118] S220, during the operation of the landslide monitoring equipment, monitoring data is obtained through the ground monitoring part and the underground monitoring part. The ground monitoring part obtains the displacement change data of the landslide body, and realizes the prediction of the landslide body according to the displacement change characteristics of the creep stage, sliding stage, and severe sliding stage of the landslide body deformation and in combination with the deep learning prediction algorithm; the underground monitoring part realizes the stability judgment of the landslide body by real-time monitoring of the soil physical parameters of the landslide body and comparing them with the critical values ​​of the soil physical parameters under the critical instability condition;

[0119] S230, when the displacement change rate of the landslide body or one of the soil physical parameters approaches the critical value, a yellow alarm is triggered, and the landslide monitoring platform sends a yellow alarm message and activates the corresponding response mechanism to increase the frequency of landslide monitoring; when the displacement change rate of the landslide body and the soil physical parameters approach the critical value at the same time, a red alarm is triggered, and the landslide monitoring platform issues a red alarm message and activates the emergency response plan to reduce the losses caused by landslide disasters.

[0120] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0121] The above specific examples are used to illustrate the present invention, which are only used to help understand the present invention and are not used to limit the present invention. For those skilled in the art to which the present invention belongs, some simple deductions, modifications or substitutions can be made according to the idea of ​​the present invention.

Claims

1. An intelligent landslide displacement monitoring and early warning system based on multi-source data, characterized in that: The system comprises a plurality of landslide monitoring devices deployed on the landslide body and a landslide monitoring platform communicatively connected to the landslide monitoring devices; The landslide monitoring equipment is used to monitor the landslide displacement, meteorological environment and physical properties of the landslide body, and upload the monitoring data to the landslide monitoring platform in real time; The landslide monitoring equipment includes a ground monitoring part and an underground monitoring part. The ground monitoring part is used to monitor the displacement and meteorological environment status of the landslide area around the clock, and the underground monitoring part is used to monitor the changes in the seepage field and stress field inside the landslide body. The landslide monitoring platform is used for landslide monitoring equipment management, monitoring task management and monitoring data management. It comprehensively processes the multi-source monitoring data obtained by the landslide monitoring equipment to achieve comprehensive monitoring, analysis and prediction of the landslide body, and provide support for landslide risk assessment and emergency management.

2. The intelligent landslide displacement monitoring and early warning system based on multi-source data according to claim 1, characterized in that: The ground monitoring part includes a GNSS monitoring station and a micro-meteorological station; The GNSS monitoring station is used to monitor the displacement changes of the landslide body in real time and obtain three-dimensional displacement data with millimeter-level accuracy through differential calculation; The micro-meteorological station is used to monitor the meteorological environment data around the landslide body.

3. The intelligent landslide displacement monitoring and early warning system based on multi-source data as claimed in claim 2 is characterized in that: The underground monitoring part includes a pore water pressure gauge and a strain gauge; The pore water pressure gauge is used to monitor the changes in pore water pressure inside the landslide body and evaluate the seepage field of the landslide body; The strain gauge is used to monitor the stress change inside the landslide body and evaluate the stability of the landslide body.

4. The intelligent landslide displacement monitoring and early warning system based on multi-source data as claimed in claim 3, characterized in that: The landslide monitoring device also includes a data communication unit and a power management unit; The data communication unit is used to transmit the collected data to the landslide monitoring platform and receive the configuration instructions and alarm information issued by the platform; The power management unit is used to support solar power supply and battery power supply to ensure continuous power supply of the device and has a wireless charging function.

5. The intelligent landslide displacement monitoring and early warning system based on multi-source data according to claim 1, characterized in that: The landslide monitoring platform specifically includes: A data receiving and storage unit, used for receiving and storing monitoring data of landslide monitoring equipment via a wireless or wired network, and supporting automatic data backup and recovery; The data processing and editing unit is used to automatically process and correct the monitoring data, automatically identify and eliminate abnormal data, use deep learning algorithms to supplement and predict missing values ​​and data change trends, and visualize the changes in monitoring data of the landslide body, supporting users to expand and fill in monitoring reports.

6. The intelligent landslide displacement monitoring and early warning system based on multi-source data according to claim 1, characterized in that: The landslide monitoring platform specifically includes: The trend analysis unit is used to use historical monitoring data, based on physical models or machine learning algorithms, to evaluate the activity of the landslide body, predict the time and scale of landslide instability, and estimate the future development trend of the landslide; based on finite element simulation experiments on the landslide body area, the critical stress parameters of landslide stability are obtained as warning values, and by monitoring the changes in underground stress values ​​and analyzing the trends of monitoring data, it helps to warn and forecast landslide disasters; The alarm unit is used to issue an alarm when the monitoring data reaches the set landslide instability threshold and send the alarm information to the relevant management department through wireless communication.

7. The intelligent landslide displacement monitoring and early warning system based on multi-source data according to claim 1, characterized in that: The landslide monitoring platform specifically includes: The user operation interactive interface is used to provide real-time monitoring data display, allowing users to view landslide monitoring data and equipment status, and supports multiple operations including remote equipment configuration, monitoring frequency adjustment, and alarm parameter setting.

8. The intelligent landslide displacement monitoring and early warning system based on multi-source data according to claim 1, characterized in that: The landslide monitoring platform specifically includes: The emergency response support unit is used to work with the local emergency management system. When a landslide warning is triggered, the emergency response mechanism is activated, and disaster loss assessment is conducted in combination with historical monitoring data to help quickly formulate emergency plans.

9. A method for implementing an intelligent landslide displacement monitoring and early warning system based on multi-source data according to any one of claims 1 to 8, characterized in that: The method comprises: Determine the monitoring area: select the approximate range of the slopes based on the high-risk slopes to be monitored, and further refine the specific areas and boundaries that need to be monitored based on various geological factors on site; Obtain basic data of the monitoring area: obtain various basic data of the monitoring area through geological survey, and determine the critical values ​​of landslide parameters when the landslide body slides through finite element simulation; Build a landslide monitoring platform: select computing servers, databases, and necessary network equipment to ensure the reliability of data processing and storage; install and configure the landslide monitoring platform software to ensure real-time data reception, processing, analysis, visualization, and alarm functions; establish a preliminary landslide prediction model based on regional characteristics and basic data, and integrate machine learning algorithms or physical models to predict landslide behavior; Arrange and install monitoring equipment: determine the installation locations of the ground monitoring part and the underground monitoring part related equipment; finally complete the installation of the data communication unit, connect the monitoring equipment data to the landslide monitoring platform, and conduct preliminary calibration and debugging to ensure accurate data transmission; Automatically complete monitoring tasks: The monitoring frequency and time of each device can be set through the landslide monitoring platform, and the monitoring task can be performed around the clock without human intervention; the landslide monitoring equipment automatically collects data on landslide displacement, meteorological environment and physical properties of the landslide body, and uploads them to the landslide monitoring platform in real time; the landslide monitoring platform automatically classifies and stores the uploaded data, and establishes a historical record of landslide monitoring data; Monitoring data analysis and processing: Through the automated data processing of the landslide monitoring platform, abnormal data is filtered, sensor errors are corrected, and data accuracy is ensured; the trend analysis function of historical data is used to identify changes in the displacement acceleration of the landslide body and assess the activity of the landslide; combined with multi-source data, machine learning or physical models are used to predict the future behavior of the landslide, focusing on the acceleration trend of the landslide, and predicting the possible time and scale of instability; when the landslide monitoring data reaches the preset instability threshold, an alarm is automatically issued, and the relevant emergency management department is notified to initiate an emergency response; Subsequent maintenance of monitoring equipment: arrange professional personnel to check the equipment regularly to ensure that the equipment is working properly; automatically issue a fault alarm when a failure occurs in the equipment or platform, and arrange personnel to repair and replace it in time; regularly check the power supply system to ensure that the monitoring equipment always has sufficient power supply.

10. The early warning method of the intelligent landslide displacement monitoring and early warning system based on multi-source data according to any one of claims 1 to 8, characterized in that: The method comprises: Conduct finite element simulation experiments on the landslide area to obtain the critical values ​​of soil physical parameters under the critical instability conditions of specific landslides as warning values; During the operation of the landslide monitoring equipment, monitoring data is obtained through the ground monitoring part and the underground monitoring part. The ground monitoring part obtains the displacement change data of the landslide body, and realizes the prediction of the landslide body based on the displacement change characteristics of the creep stage, sliding stage, and severe sliding stage of the landslide body deformation and combined with the deep learning prediction algorithm; the underground monitoring part realizes the stability judgment of the landslide body by real-time monitoring of the soil physical parameters of the landslide body and comparing them with the critical values ​​of the soil physical parameters under critical instability conditions; When the displacement change rate of the landslide body or one of the soil physical parameters approaches the critical value, a yellow alarm is triggered. The landslide monitoring platform sends a yellow alarm message and activates the corresponding response mechanism to increase the frequency of landslide monitoring. When the displacement change rate of the landslide body and the soil physical parameters approach the critical value at the same time, a red alarm is triggered. The landslide monitoring platform issues a red alarm message and activates the emergency response plan to reduce the losses caused by landslide disasters.

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