An Adaptive Monitoring System and Method for Landslide Crack Displacement

Abstract

The present invention discloses a landslide crack displacement adaptive monitoring system and method. The system includes a monitoring and early warning cloud platform, a wireless gateway, and a plurality of node monitoring devices arranged at different landslide monitoring points. The node monitoring device includes a microprocessor, a displacement sensor connected to the microprocessor, a LoRa wireless communication module, and a power supply module. The microprocessor is communicatively connected to the wireless gateway through the LoRa wireless communication module. The wireless gateway is communicatively connected to the monitoring and early warning cloud platform through a 4G / NB-IoT network. The monitoring and early warning cloud platform is communicatively connected to a customer terminal. The landslide crack displacement adaptive monitoring system and method provided by the present invention perform remote monitoring of multiple monitoring points through a wireless network, realizing the whole-process all-weather real-time intelligent monitoring and early warning of crack displacement, and improving the reliability and safety of landslide disaster monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and particularly to a landslide crack displacement adaptive monitoring system and method. Background Art

[0002] Landslides are characterized by wide distribution, frequent occurrence, slowness, and concealment. Once they occur, they will cause casualties and huge economic losses. Landslide monitoring can reduce disaster losses. Landslide disaster monitoring obtains the activity state data of the landslide body through monitoring instruments, grasps the evolution process of the landslide disaster, and provides reliable data for the prediction, analysis, evaluation, and prevention and control projects of the landslide disaster.

[0003] With the development of science and technology, the automation degree and intelligent level of landslide crack displacement monitoring have also been continuously improved. The change in displacement is still an important index parameter in landslide monitoring. Then, whether various monitoring means can effectively ensure the reliability and safety of the landslide crack displacement monitoring system, and whether they can obtain the law and safety status of the landslide crack displacement change all-weather and completely, has gradually become the goal of the gradual improvement of each monitoring technology means. Summary of the Invention

[0004] The purpose of the present invention is to provide a landslide crack displacement adaptive monitoring system and method, which remotely monitors multiple monitoring points through a wireless network, realizes the whole-process all-weather real-time intelligent monitoring and early warning of crack displacement monitoring, and improves the reliability and safety of landslide disaster monitoring.

[0005] To achieve the above purpose, the present invention provides the following solutions:

[0006] A landslide crack displacement adaptive monitoring system, the system includes: a monitoring and early warning cloud platform, a wireless gateway, and a plurality of node monitoring devices arranged at different landslide monitoring points. The node monitoring device includes a microprocessor, a displacement sensor, a LoRa wireless communication module, and a power supply module connected to the microprocessor; the microprocessor is communicatively connected to the wireless gateway through the LoRa wireless communication module, the wireless gateway is communicatively connected to the monitoring and early warning cloud platform through a 4G / NB-IoT network, and the monitoring and early warning cloud platform is communicatively connected to a customer terminal.

[0007] Optionally, the displacement sensor adopts a rope-pulling coaxial photoelectric coding sensor; the microprocessor adopts an STM32 microprocessor; the LoRa wireless communication module adopts an SX1278 radio frequency module.

[0008] Optionally, a DC operational amplifier is connected between the displacement sensor and the microprocessor to eliminate the measurement error caused by the zero offset of the displacement sensor and its temperature drift.

[0009] Optionally, the node monitoring device is further provided with an audible and visual alarm, and the audible and visual alarm is connected to the microprocessor.

[0010] Optionally, the node monitoring device is further provided with a GPS positioning module, and the GPS positioning module is connected to the microprocessor.

[0011] Optionally, the node monitoring device is further provided with a temperature sensor and a humidity sensor, and the temperature sensor and the humidity sensor are connected to the microprocessor.

[0012] Optionally, the power supply module is a solar cell module, and the solar cell module includes a solar panel, a photovoltaic controller and a storage battery. The solar panel is connected to the storage battery through the photovoltaic controller, and the storage battery supplies power to the node monitoring device; a power grid power supply interface is further provided on the node monitoring device for electrically connecting to the power grid system.

[0013] The present invention also provides a landslide crack displacement adaptive monitoring method, which is applied to the above-mentioned landslide crack displacement adaptive monitoring system, and includes the following steps:

[0014] Real-time collect the displacement value of the landslide monitoring point, and fuse two filtering methods of "median filtering method" + "arithmetic mean filtering method" to eliminate the deviation of the collected displacement value, and obtain the displacement change amount;

[0015] Compare the displacement change amount with a preset threshold;

[0016] If the preset threshold is not reached, the node monitoring device is in the listening working mode, the main frequency of the microprocessor is 65KHz, the communication interface is closed, and each functional module is in a low-speed running state;

[0017] If the preset threshold is exceeded, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, the different acquisition time intervals are adaptively adjusted according to the displacement change amount, the main frequency of the microprocessor is 16MHz, and the energy consumption is optimized in this mode;

[0018] The microprocessor sends the displacement change amount exceeding the preset threshold to the monitoring and early warning cloud platform. After the monitoring data is successfully sent, it enters the low-speed running state, the main frequency of the microprocessor drops from 16MHz to 65KHz, and enters the listening working mode after a time period.

[0019] Optionally, the real-time collection of the displacement value of the landslide monitoring point, and the fusion of two filtering methods of "median filtering method" + "arithmetic mean filtering method" to eliminate the deviation of the collected displacement value, and obtain the displacement change amount, specifically includes:

[0020]

[0021] Among them, S i is to collect n data at a single time, and the i-th collected data is the median value taken; is the average value of the data collected during this time period; ΔS is the displacement change.

[0022] Optionally, if it exceeds the preset threshold, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, and the different acquisition time intervals are adaptively adjusted according to the displacement change. The main frequency of the microprocessor is 16 MHz, and the energy consumption is optimized in this mode. Specifically, it includes:

[0023] Define the change amount of the monitoring data of the running state of the landslide crack displacement adaptive monitoring system from large to small, and set the maximum allowable change amount, two intermediate allowable change amounts to the minimum allowable change amount in turn: Δs max 、Δs mid 、Δs min 、Δs mini ; The maximum data acquisition interval allowed by the monitoring system is T max and the minimum data acquisition interval T min ;

[0024] When the displacement change amount exceeds the maximum allowable change amount ΔS max , that is, ΔS>ΔS max , the displacement monitoring data acquisition interval is reduced, and the acquisition time interval T i+1 is:

[0025]

[0026] That is:

[0027] Among them, T i is the acquisition time interval before adjustment;

[0028] When the acquired displacement change amount exceeds the allowable change amount ΔS mid , that is, ΔS mid <ΔS<ΔS max The displacement monitoring data acquisition interval is reduced, and the acquisition time interval T i+1 is:

[0029]

[0030] That is:

[0031]

[0032] When the acquired displacement change amount exceeds the allowable change amount ΔS min , that is, ΔS min <ΔS<ΔSmid The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0033]

[0034] That is:

[0035]

[0036] When the displacement change amount collected exceeds the allowable change amount ΔS mini , that is, ΔS mini <ΔS<ΔS min The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0037]

[0038] That is:

[0039]

[0040] When the displacement change amount collected does not exceed the allowable change amount ΔS mini , that is, ΔS<ΔS mini The acquisition interval of displacement monitoring data is increased, and the acquisition time interval T i+1 is:

[0041]

[0042] That is:

[0043]

[0044] According to the specific embodiments provided by the present invention, the following technical effects are disclosed: The landslide crack displacement adaptive monitoring system and method provided by the present invention are aimed at the monitoring requirements of landslide disaster crack displacement. High-precision crack displacement sensors are developed by using optoelectronic coding and digital filtering technologies to accurately sense the crack displacement deformation. A low-power adaptive data acquisition unit, a control unit, and a power supply unit with an STM32 embedded microprocessor as the core are developed. An intelligent data acquisition algorithm mainly based on trigger acquisition and encryption acquisition strategies is designed. Combined with 4G / NB-IoT / LoRa wireless communication technologies, a multi-network hybrid transmission technology with reliable, stable, and full-coverage data transmission is formed. The collaborative linkage technology between the landslide crack displacement monitoring and warning instrument and other parameter monitoring and warning devices and the monitoring and warning platform on the landslide body is studied to form an intelligent linkage monitoring method between instrument-instrument and instrument-platform. The structure and appearance process of the landslide intelligent monitoring and warning instrument are designed with the design concepts of integration, modularization, assembly, and integration. A monitoring system with light weight, small volume, low power consumption, high protection level, easy installation, and stable performance is developed to achieve multi-means warning such as broadcast, SMS, phone call, and WeChat. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 It is the structural block diagram of the landslide crack displacement adaptive monitoring system in the embodiment of the present invention;

[0047] Figure 2 It is the working flowchart of the landslide crack displacement adaptive monitoring method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0049] The purpose of the present invention is to provide a landslide crack displacement adaptive monitoring system and method, which remotely monitors multiple monitoring points through a wireless network to realize the whole-process all-weather real-time intelligent monitoring and warning of crack displacement, and improves the reliability and safety of landslide disaster monitoring.

[0050] To make the above objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Figure 1 This is the structural block diagram of the landslide crack displacement adaptive monitoring system according to an embodiment of the present invention. As Figure 1 shown, the landslide crack displacement adaptive monitoring system provided by the embodiment of the present invention includes: a monitoring and warning cloud platform, a wireless gateway, and a plurality of node monitoring devices arranged at different landslide monitoring points. The node monitoring device includes a microprocessor, a displacement sensor, a LoRa wireless communication module, and a power supply module connected to the microprocessor; the microprocessor is communicatively connected to the wireless gateway through the LoRa wireless communication module, the wireless gateway is communicatively connected to the monitoring and warning cloud platform through a 4G / NB-IoT network, and the monitoring and warning cloud platform is communicatively connected to a customer terminal. The customer terminal is a mobile phone, a computer, etc., and can publish warning information in the APP.

[0052] Among them, the displacement sensor uses a rope-pulling coaxial photoelectric coding sensor; the microprocessor uses an STM32 microprocessor; the LoRa wireless communication module uses an SX1278 radio frequency module.

[0053] A DC operational amplifier is connected between the displacement sensor and the microprocessor to eliminate the measurement error caused by the zero offset and temperature drift of the displacement sensor.

[0054] The node monitoring device is also provided with an audible and visual alarm, and the audible and visual alarm is connected to the microprocessor.

[0055] The node monitoring device is also provided with a GPS positioning module, and the GPS positioning module is connected to the microprocessor. The node monitoring device is also provided with a temperature sensor and a humidity sensor, and the temperature sensor and the humidity sensor are connected to the microprocessor.

[0056] The power supply module is a solar cell module, and the solar cell module includes a solar panel, a photovoltaic controller, and a storage battery. The solar panel is connected to the storage battery through the photovoltaic controller, and the storage battery supplies power to the node monitoring device; a grid power supply interface is also provided on the node monitoring device for electrically connecting to the power grid system.

[0057] The landslide crack displacement adaptive monitoring system provided by the present invention uses a rope-pulling coaxial photoelectric coding sensor as the displacement sensor. During the measurement process, since the sensor may be affected by the environment, the internal components will have zero-point offset and temperature drift, and there will be measurement errors when the input is zero. To eliminate this error, a DC operational amplifier needs to be added at the front end of the sensor, which is a composite amplifier that uses digital circuits to dynamically correct analog offset errors; develop a long-distance and low-power data transmission technology - LoRa communication technology, formulate a networking protocol, and form a low-power wireless sensor network; integrate wireless communication public 4G / NB-IoT to ensure barrier-free and reliable data transmission in emergency situations, research low-power and high-reliability multi-mode instant communication nodes and gateway devices suitable for landslide crack displacement monitoring, and meet the real-time continuous working requirements in emergency situations; optimize the design of each functional module of the node monitoring device, adopt an embedded panel structure, select lightweight, high-strength, and corrosion-resistant materials to make the instrument shell, reasonably partition and plan each functional module to improve the space utilization rate, and achieve easy assembly, maintenance, and portability and simple operation.

[0058] As Figure 2 shown, the present invention also provides a landslide crack displacement adaptive monitoring method, which is applied to the above-mentioned landslide crack displacement adaptive monitoring system, and includes the following steps:

[0059] Collect the displacement values of the landslide monitoring points in real time, and fuse the two filtering methods of "median value filtering method" + "arithmetic mean filtering method" to eliminate the deviation of the collected displacement values, and obtain the displacement change;

[0060] Compare the displacement change with a preset threshold;

[0061] If the preset threshold is not reached, the node monitoring device is in the listening working mode, the main frequency of the microprocessor is 65KHz, the communication interface is closed, and each functional module is ensured to be in a low-speed operation state;

[0062] If it exceeds the preset threshold, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, and the different acquisition time intervals are adaptively adjusted according to the displacement change. The main frequency of the microprocessor is 16MHz, and the energy consumption is optimized in this mode;

[0063] The microprocessor sends the displacement change exceeding the preset threshold to the monitoring and early warning cloud platform. After the monitoring data is successfully sent, it enters the low-speed operation state, the main frequency of the microprocessor drops from 16MHz to 65KHz, and it enters the listening working mode after a time period.

[0064] Among them, the displacement value of the landslide monitoring point is collected in real time, and the collected displacement value is processed to eliminate deviation by integrating two filtering methods of "median filtering method" + "arithmetic mean filtering method" to obtain the displacement change amount, which specifically includes:

[0065]

[0066] Among them, S i is to collect n data for a single time, and the i-th collected data is the median value taken; is the average value of the data collected during this time period; ΔS is the displacement change amount. The median average filtering method is adopted, that is, at a certain data collection node, n data are collected. For example, n = 15. One maximum value and one minimum value are removed from the collected data, and the average value of the remaining data is calculated. Therefore, this algorithm does not require sorting the data. The key is to calculate the maximum value, the minimum value, sum the remaining data, and then calculate the average value. By integrating the advantages of the "median filtering method" + "arithmetic mean filtering method", for the occasionally occurring pulsed interference, the sampling value deviation caused by it can be eliminated.

[0067] Among them, if it exceeds the preset threshold, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, and the different acquisition time intervals are adaptively adjusted according to the displacement change amount. The main frequency of the microprocessor is 16MHz. In this mode, the energy consumption is optimized, which specifically includes:

[0068] Define the change amount of the monitoring data of the running state of the landslide crack displacement adaptive monitoring system from large to small, and set the maximum allowable change amount, two intermediate allowable change amounts to the minimum allowable change amount in turn: Δs max 、Δs mid 、Δs min 、Δs mini ; The maximum data acquisition interval allowed by the monitoring system is T max and the minimum data acquisition interval T min ;

[0069] When the displacement change amount exceeds the maximum allowable change amount ΔS max , that is, ΔS > ΔS max , the acquisition interval of the displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0070]

[0071] That is:

[0072] Among them, T i is the acquisition time interval before adjustment;

[0073] When the collected displacement change exceeds the allowable change ΔS mid , that is, ΔS mid <ΔS<ΔS max The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0074]

[0075] That is:

[0076]

[0077] When the collected displacement change exceeds the allowable change ΔS min , that is, ΔS min <ΔS<ΔS mid The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0078]

[0079] That is:

[0080]

[0081] When the collected displacement change exceeds the allowable change ΔS mini , that is, ΔS mini <ΔS<ΔS min The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is:

[0082]

[0083] That is:

[0084]

[0085] When the collected displacement change does not exceed the allowable change ΔS mini , that is, ΔS<ΔS mini The acquisition interval of displacement monitoring data is increased, and the acquisition time interval T i+1 is:

[0086]

[0087] That is:

[0088]

[0089] It can be seen from equations (3), (5), (7), (9), and (11) that the change in landslide displacement directly affects the size of the data acquisition interval. Even a tiny change can also cause a change in the data acquisition interval. This condition set aims to adopt differential acquisition, reduce the amount of data acquisition, and improve the acquisition efficiency. Different amounts of displacement change require different data acquisition frequencies, achieving stress-adjusted acquisition intervals, intelligent perception throughout the process from slow deformation to accelerated change, adaptive adjustment, improving data acquisition quality control, and smooth autonomous control and adjustment of acquisition frequency recognition. Landslide crack displacement monitoring is an uncertain and non-linear event with limitations in modeling methods, so an adaptive event-triggered control acquisition strategy is proposed. Design an evaluation decision set to implement the event-triggered control mechanism, flexibly combine the event-triggered mechanism, and determine how to adjust the sampling / execution data acquisition interval so that the switched event-triggered control can effectively handle non-linearity, uncertainty, and sampling / execution errors, thereby achieving system stability and reliability.

[0090] The landslide crack displacement adaptive monitoring method provided by the present invention uses the adaptive event-triggered control theory as a means to improve the triggering perception ability of the displacement change of the monitoring device, autonomously plan the monitoring strategy, and control power consumption. That is, through intelligent "perception - decision - adjustment - planning - action" on the frequent changes of the monitoring system itself, it autonomously switches different sampling density strategies in different value ranges, and through the adaptive data acquisition algorithm, fully considers low-power data acquisition and multi-task parallel operation, realizes intelligent perception throughout the process of crack deformation from deformation to accelerated change, adaptively adjusts the sampling frequency, controls the sampling density, realizes smooth autonomous control and adjustment, realizes encrypted acquisition frequency recognition, controls the working frequency of the STM32 microprocessor, and reduces power consumption.

[0091] In this article, specific examples are used to elaborate on the principle and implementation method of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for adaptively monitoring landslide crack displacement, which is applied to a landslide crack displacement adaptive monitoring system, is characterized in that The landslide crack displacement adaptive monitoring system includes: a monitoring and early warning cloud platform, a wireless gateway, and a plurality of node monitoring devices arranged at different landslide monitoring points. The node monitoring device includes a microprocessor, a displacement sensor, a LoRa wireless communication module, and a power supply module connected to the microprocessor. The microprocessor is communicatively connected to the wireless gateway through the LoRa wireless communication module. The wireless gateway is communicatively connected to the monitoring and early warning cloud platform through a 4G / NB-IoT network. The monitoring and early warning cloud platform is communicatively connected to a customer terminal. The landslide crack displacement adaptive monitoring method includes the following steps: Collect the displacement value of the landslide monitoring point in real time, and fuse the two filtering methods of "median value filtering method" + "arithmetic mean filtering method" to eliminate the deviation of the collected displacement value, and obtain the displacement change amount. Compare the displacement change amount with a preset threshold. If the preset threshold is not reached, the node monitoring device is in the listening working mode, the main frequency of the microprocessor is 65KHz, the communication interface is closed, and each functional module is ensured to run at a low speed. If the preset threshold is exceeded, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, and different acquisition time intervals are adaptively adjusted according to the displacement change amount. The main frequency of the microprocessor is 16MHz, and the energy consumption is optimized in this mode. The microprocessor sends the displacement change amount exceeding the preset threshold to the monitoring and early warning cloud platform. After the monitoring data is successfully sent, it enters the low-speed operation state, the main frequency of the microprocessor drops from 16MHz to 65KHz, and it enters the listening working mode after a time period. The above-mentioned "If the preset threshold is exceeded, the node monitoring device is in the normal working mode, the communication is turned on to ensure smooth data transmission, and different acquisition time intervals are adaptively adjusted according to the displacement change amount. The main frequency of the microprocessor is 16MHz, and the energy consumption is optimized in this mode" specifically includes: Define the change amount of the monitoring data of the operation status of the landslide crack displacement adaptive monitoring system from large to small, and sequentially set the maximum allowable change amount, two intermediate allowable change amounts to the minimum allowable change amount: ΔS max , ΔS mid , ΔS min , ΔS mini ; The maximum data acquisition interval allowed by the monitoring system is T max ; When the displacement change amount exceeds the maximum allowable change amount ΔS max , that is, ΔS > ΔS max , the acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is as follows: Among them, T i is the acquisition time interval before adjustment; ΔS is the displacement change; When the collected displacement change exceeds the allowable change ΔS mid , that is, ΔS mid <ΔS<ΔS max The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is: When the collected displacement change exceeds the allowable change ΔS min , that is, ΔS min <ΔS<ΔS mid The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is: When the collected displacement change exceeds the allowable change ΔS mini , that is, ΔS mini <ΔS<ΔS min The acquisition interval of displacement monitoring data is reduced, and the acquisition time interval T i+1 is: When the collected displacement change does not exceed the allowable change ΔS mini , that is, ΔS < ΔS mini The acquisition interval of displacement monitoring data increases, and the acquisition time interval T i+1 is:

2. The landslide crack displacement adaptive monitoring method according to claim 1, wherein, The displacement sensor adopts a rope-pulling coaxial photoelectric encoder sensor; the microprocessor adopts an STM32 microprocessor; the LoRa wireless communication module adopts an SX1278 radio frequency module.

3. The landslide crack displacement adaptive monitoring method according to claim 1, characterized in that A DC operational amplifier is connected between the displacement sensor and the microprocessor to eliminate the measurement error caused by the zero-point offset and temperature drift of the displacement sensor.

4. The landslide crack displacement adaptive monitoring method according to claim 1, wherein The node monitoring device is also provided with an audible and visual alarm, and the audible and visual alarm is connected to the microprocessor.

5. The landslide crack displacement adaptive monitoring system according to claim 1, wherein The node monitoring device is also provided with a GPS positioning module, and the GPS positioning module is connected to the microprocessor.

6. The landslide crack displacement adaptive monitoring method according to claim 1, characterized in that The node monitoring device is also provided with a temperature sensor and a humidity sensor, and the temperature sensor and the humidity sensor are connected to the microprocessor.

7. The landslide crack displacement adaptive monitoring method according to claim 1, wherein The power supply module is a solar cell module. The solar cell module includes a solar panel, a photovoltaic controller, and a storage battery. The solar panel is connected to the storage battery through the photovoltaic controller, and the storage battery supplies power to the node monitoring device. A grid power supply interface is also provided on the node monitoring device for electrically connecting to the grid system.

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