CN112927478A - Geological disaster universal monitoring and early warning system - Google Patents

Geological disaster universal monitoring and early warning system Download PDF

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CN112927478A
CN112927478A CN202011609221.0A CN202011609221A CN112927478A CN 112927478 A CN112927478 A CN 112927478A CN 202011609221 A CN202011609221 A CN 202011609221A CN 112927478 A CN112927478 A CN 112927478A
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胡辉
林兴立
张世元
胡荣
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Guangzhou Hannan Engineering Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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Abstract

The invention discloses a geological disaster universal monitoring and early warning system, which comprises universal monitoring equipment and a geological disaster universal monitoring cloud platform, wherein the universal monitoring equipment and the geological disaster universal monitoring cloud platform are in communication connection; the geological disaster pervasive monitoring cloud platform comprises an Internet of things equipment management system, a monitoring data analysis and early warning system, a database and a data visualization application platform, and the monitoring data analysis and early warning system, the database and the data visualization application platform are connected with each other through data interfaces; the universal monitoring equipment combines the built-in sensor module with the external expansion interface, so that the equipment has stronger expansibility and wider applicability; the data visualization application platform and the monitoring data analysis and early warning system issue configuration updating instructions to the universal monitoring equipment through the Internet of things equipment management system according to different stages of geological disaster dangerous case inoculation, change the data uploading period and frequency, dynamically monitor disaster area data in real time, and greatly improve the early warning accuracy according to an optimized early warning judgment flow; through man-machine interactive management, the disaster is timely treated, and casualties and property loss are reduced.

Description

Geological disaster universal monitoring and early warning system
Technical Field
The invention relates to the technical field of monitoring and early warning systems, in particular to a geological disaster universal monitoring and early warning system.
Background
The general and professional monitoring are defined as follows:
the universal equipment is automatic monitoring equipment for monitoring indexes such as slope disaster earth surface deformation and rainfall, comprises measurement items such as displacement, cracks, inclination angles, acceleration, water content and rainfall, and has the characteristics of simple function, proper precision, reliable operation, lower cost and strong popularization applicability.
Present general type monitoring facilities adopts the mode of many sensing intelligent monitoring appearance more, monitor geological conditions, it is with low costs to have the hardware, the deployment is simple and easy, advantage such as duration is long, but at the in-service use in-process, because the geological conditions in different regions is different, it is various to have geological disasters to induce the reason, disaster degree is difficult to condition such as simple early warning, current general type monitoring facilities monitoring physical quantity index is single, can't expand according to the data physics monitoring needs of different regional geological conditions or the different section positions in same region, be difficult to satisfy complicated changeable early warning requirement.
In addition, in actual operation, the general monitoring equipment usually adopts a working mode with lower monitoring frequency, for example, the measurement and data transmission are carried out once in 1-2 hours, actually, the working period within 1-2 hours is only several minutes, the rest of the time sensors and the communication modules are in a power-off dormant state, and only the operation of a clock circuit inside the single chip microcomputer is reserved, so that the equipment is awakened to carry out data acquisition, data transmission and other actions within fixed time, and if a monitoring object is greatly deformed during the period, the measurement and early warning cannot be carried out in time. Therefore, the general equipment is not practically suitable for application scenes (such as high-sudden monitoring scenes of boulders, dangerous rock collapse and the like) which need high monitoring frequency when entering a dangerous period, the early warning timeliness is reduced, and related personnel cannot respond to dangerous cases quickly. Therefore, the problems are solved by the invention or the improvement on the equipment structure, the system operation method and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and the invention aims to form a set of geological disaster universal monitoring and early warning system which is advanced, stable, reliable, efficient and practical in technology according to the requirement of monitoring and early warning by utilizing geological disaster universal equipment, combines specifications and fully utilizes new technologies such as Internet of things, cloud platforms and the like.
The purpose of the invention is realized by the following technical scheme: the utility model provides a general type of geological disasters monitors early warning system which characterized in that: the universal monitoring and early warning system comprises universal monitoring equipment and a universal monitoring cloud platform for geological disasters; the universal monitoring equipment is in communication connection with the universal monitoring cloud platform for the geological disasters; the universal monitoring equipment comprises a computing storage Module (MCU), a built-in sensor module, a power management module, a wireless communication module, a positioning module and an external expansion interface; the built-in sensor module also comprises a built-in tilt angle sensor module, a built-in stay wire displacement sensor module and a built-in triaxial accelerometer module; the geological disaster pervasive monitoring cloud platform comprises an Internet of things equipment management system, a monitoring data analysis and early warning system, a database and a data visualization application platform, wherein the Internet of things equipment management system, the monitoring data analysis and early warning system, the database and the data visualization application platform are connected through data interfaces; the universal monitoring equipment is used for uploading monitored data to the Internet of things equipment management system and receiving and executing instructions issued by the Internet of things equipment management system; the Internet of things equipment management system analyzes the received data and stores the analyzed data into a database, and a configuration updating file input in a monitoring data analysis and early warning system or a data visualization application platform is issued to the universal monitoring equipment in a command form for configuration updating; the monitoring data analysis and early warning system is responsible for monitoring data calculation processing, analysis and judgment, early warning configuration, early warning triggering, early warning release and storing the data into a database; the database stores relevant data of the universal monitoring equipment, the Internet of things equipment management system, the monitoring data analysis and early warning system and the data visualization application platform; the data visualization application platform is used for realizing man-machine interactive operation.
Preferably, the universal monitoring device further comprises an external charging interface.
Preferably, the power management module comprises a lithium battery pack, a solar controller, a voltage detection module and a power output control.
Preferably, the external expansion interface is connected with an alarm device.
Preferably, the external expansion interface is connected with an external sensor.
Preferably, the external sensor comprises a fixed inclinometer, a rain gauge, a soil moisture content meter, a static level gauge, a laser range finder, a mud level meter, an underground water level meter, a stay wire displacement, an inclination angle, temperature and humidity and a vibrating wire type sensor.
Preferably, the data visualization application platform comprises a data query and visualization unit, an early warning release unit, an early warning and alarm elimination processing unit, a data information superposition GIS map display unit, a data information superposition three-dimensional model display unit and a data statistics unit.
Preferably, the early warning issuing unit establishes network communication with a vehicle-mounted GPS navigation system, and the vehicle-mounted GPS navigation system receives early warning information sent by the early warning issuing unit.
A method based on a general monitoring and early warning system for geological disasters comprises the following specific working steps:
s1: the method comprises the steps that a universal monitoring device reads configuration information of an Internet of things device management system, and acquires original data to upload to a cloud platform according to configuration requirements;
s2: the Internet of things equipment management system checks, analyzes and splits the data of the universal monitoring equipment, stores the data into the database in a classified manner, and receives the instruction from the database and sends the instruction to the universal monitoring equipment;
s3: after the monitoring data analysis and early warning system receives the data in the database, calculating a monitoring result value, when the monitoring result value reaches a configuration updating threshold value, issuing a configuration updating instruction to the Internet of things equipment management system, modifying parameter configuration, and shortening parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like;
comparing the monitoring result value with a grading early warning threshold value, carrying out comprehensive early warning, and issuing a data receiving success instruction when the result value does not reach the comprehensive early warning grade;
when the comprehensive early warning level reaches an early warning state, the monitoring data analysis and early warning system sends a configuration updating instruction, parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like are modified into uninterrupted acquisition, and the universal monitoring equipment acquires return data in real time;
when the comprehensive early warning level reaches an early warning state or the comprehensive early warning level is changed compared with the previous period, sending a configuration updating instruction, generating an early warning short message according to a preset format and sending the early warning short message to related personnel;
meanwhile, the monitoring data analysis and early warning system stores all information into a database;
s4: the universal monitoring equipment modifies the new configuration parameters according to the instruction issued by the equipment management system of the internet of things and executes the step S1 according to the instruction;
s5: the data visualization application platform triggers the monitoring point positioning icon identification early warning color block on the GIS and flickers according to the early warning information;
s6: after receiving the early warning prompt, the platform administrator user checks the early warning data and checks the equipment condition, and then confirms whether the alarm condition is a real dangerous condition or not; if the alarm condition is a real alarm condition, the engineering manager user marks the real alarm condition in the platform, and the data visualization platform pushes the information and the data of the real alarm condition to the account number of the engineering related party personnel to realize the release of the alarm condition; if the alarm is false alarm, the engineering manager user marks the alarm as false alarm in the platform, the data visualization platform closes the early warning state, returns to the normal monitoring mode, and does not issue the alarm to the personnel of the engineering related party; after receiving the early warning information, the management personnel of the engineering related party further confirms the alarm condition in the data visualization management platform, and if the alarm condition is real, the data visualization management platform calls an emergency plan from the database for the management personnel of the engineering related party to use for reference; if the management personnel of the relevant engineering party judge that the alarm is false, the data visualization management platform closes the early warning state and returns to the normal monitoring mode;
s7: and the platform administrator confirms that the data monitoring frequency and period need to be changed, a configuration updating instruction is issued to the Internet of things equipment management system, and the universal monitoring equipment executes the step S4.
Preferably, the specific process of step S1 of the method is as follows:
s1.1, starting a period, receiving a configuration instruction sent by an Internet of things management system by a universal monitoring device to set configuration information, electrifying an internal sensor and an external interface, acquiring data after the electrifying stability time is reached, and initially starting threshold judgment:
s1.2, if the threshold value exceeds a preset threshold value, starting a power supply of the wireless communication module, establishing network communication with an Internet of things management system, and sending monitoring data;
s1.3, if the current period does not exceed a preset threshold, comparing whether the current period is a data uploading period, and if the current period is the data uploading period, turning on a power supply of the wireless communication module and sending monitoring data to the cloud platform; if the data is not in the data uploading period, after a data receiving success instruction replied by the cloud platform is received, the power supply of the sensor and the external interface is cut off, and the cloud platform enters a dormant state;
s1.4, if the cloud platform replies a configuration updating instruction or an early warning instruction after receiving the data, applying configuration updating or triggering an acousto-optic alarm module of an external interface, and then entering a dormant state.
S1.5, starting the next period, applying the latest configuration information to carry out data acquisition, and executing the steps S1.1-S1.4.
Preferably, the monitoring result value calculating method in step S3 includes:
1) inclination angle monitoring data processing and threshold value comparison process
Reading the original inclination angle monitoring data from a database:
θx-an X angle (°) measured by the tilt sensor;
θy-the Y angle (°) measured by the tilt sensor;
second, calculate the total inclination angle theta of the sensorz：
Figure RE-GDA0003050223060000041
And thirdly, calculating the change of the inclination angle:
subtracting the previous period measurement value from the current measurement value of the inclination angles of the x, y and z three axes to obtain the current inclination angle variation;
Δθ(i)＝θ(i)-θ(i-1)
fourthly, calculating the accumulated inclination angle change:
subtracting the measurement value at initial zero setting from the current measurement value of the inclination angles of the x, y and z three axes to obtain the accumulated variation of the inclination angles;
∑θ(i)＝θ(i)-θ(0)
calculating the daily cumulative change rate of the inclination angle on the day:
subtracting the measurement value of the last acquisition period of the previous day of the axis from the measurement value of the inclination angle of the x, y and z axes to obtain the current accumulated variation;
recording the first acquisition period of the day as T1The last acquisition period of the previous day is T0The inclination angle of the X changes in the same day:
Δθ(T1)＝∑θ(T1)-∑θ(T0)
Δθ(Ti)＝∑θ(Ti)-∑θ(Ti-1)+Δθ(Ti-1)
based on total inclination angle theta of sensorzCalculating the accumulated relative displacement of the dangerous rock mass, the boulder or the shallow landslide:
accumulating the horizontal displacement:
∑H(i)＝L*sinθz(i)-L*sinθz(0)
accumulating the vertical displacement:
∑V(i)＝L*cosθz(i)-L*cosθz(0)
wherein, L represents the rigid body characteristic length of the dangerous rock mass and the boulder;
when the method is used for shallow slope measurement, L represents the length of the steel pipe driven below the slope surface;
and seventh, warning and judging: respectively comparing the calculation results with preset grading early warning threshold values, wherein early warning grades are classified from high to low by I-IV grades; after comparison, endowing each monitoring result parameter with a corresponding early warning level; finally, taking the early warning level with higher grade as the early warning level of the monitoring point;
after early warning judgment is finished, storing the calculation result of the monitoring data and early warning level information into a database;
2) stay wire displacement data processing and threshold value comparison process
Reading original data of stay wire displacement monitoring from a database: stay length measurement S (mm)
Secondly, calculating the displacement: the measured value of the length of the stay wire in the last period is subtracted from the measured value of the length of the stay wire in the current period, and the formula is as follows
ΔS(i)＝S(i)-S(i-1)
Calculating the accumulated displacement: the measured value of the stay length at the initial zero setting is subtracted from the measured value of the stay length at this time, and the formula is as follows
∑S(i)＝S(i)-S(0)
Fourthly, calculating daily change rate of the displacement on the day:
recording the first acquisition period of the day as t1The last collection period of the previous day is t0Cumulative shift (mm) of the day:
ΔS(t1)＝∑S(t1)+∑S(t0)
ΔS(ti)＝∑S(ti)-∑S(ti-1)+ΔS(ti-1)
calculating the accumulated horizontal displacement and the accumulated vertical displacement by combining the monitoring data of the sensor oblique angle theta
Accumulating the horizontal displacement:
∑H(i)＝∑s(i)*sinθ
accumulating the vertical displacement:
∑V(i)＝∑s(i)*cosθ
wherein theta represents an inclination angle measurement value parallel to the stretching direction of the stay wire displacement sensor;
or selecting the theta of the tilt sensor according to the deployment orientation of the general monitoring equipmentxOr thetayAs θ;
sixthly, early warning judgment: respectively comparing the calculation results with a preset grading early warning threshold value, wherein the early warning grades are classified from high to low by I-IV, after comparison, giving the corresponding early warning grade to each monitoring result parameter, and finally taking the early warning grade with a higher grade as the early warning grade of the monitoring point;
seventhly, after the early warning judgment is finished, the monitoring data calculation result and the early warning level information are stored in a database;
3) acceleration data processing and threshold comparison process
Acceleration data sampling: the acquisition of acceleration period data can be controlled by controlling the power-on time, the time for once power-on acquisition, the acquisition interval and the sampling frequency can be set, and the data are transmitted to a cloud platform database for storage after the data reach a data transmission period;
secondly, reading time-domain acceleration time-domain data of a time period from a database by a monitoring data analysis and early warning system process, converting the time-domain data of the acceleration into frequency-domain data after the time-domain data is processed by an integral and Fourier transform algorithm, and obtaining frequency spectrum data with the abscissa as frequency/Hz and the ordinate as amplitude;
thirdly, the natural vibration frequency perpendicular to the sliding surface direction of the dangerous rock mass is represented by extracting the maximum amplitude and the corresponding frequency in the frequency spectrum data, and the maximum amplitudes of the two adjacent vibrations are respectively recorded as A1、A2Corresponding frequencies are respectively fd(t1)、fd(t2) The corresponding moments of the two vibrations are t1、t2And calculating the damping ratio xi and the undamped vibration frequency f in the direction vertical to the sliding surface of the dangerous rock mass according to the following formula:
Figure RE-GDA0003050223060000061
Figure RE-GDA0003050223060000062
fourthly, the calculated undamped vibration frequency f represents the undamped self-vibration frequency of the monitored dangerous rock mass, the bonding area of the dangerous rock mass and the bedrock is gradually reduced along with the development of the cracks of the dangerous rock mass, the self-vibration frequency f is gradually reduced, when f is lower than a critical value, the dangerous rock collapses, the calculated result f is compared with a preset grading early warning threshold value, the early warning grade is classified from high to low into I-IV grade, and after comparison, the monitoring result parameters are endowed with corresponding early warning grades;
and fifthly, after the early warning judgment is finished, storing the calculation result of the monitoring data and the early warning level information into a database.
The invention has the following advantages:
1) the front-end universal monitoring equipment is only responsible for simple functions such as data acquisition, preliminary judgment, data reporting and the like, and is not responsible for data processing, and the responsible data processing part is handed over to the rear-end cloud platform for processing. The division work cooperation of the front end and the cloud platform is realized, the front-end universal equipment can operate at low power consumption, and the cloud platform is used for realizing more complex analysis, calculation and early warning functions. The adaptability of the universal monitoring system and the monitoring and early warning speciality are improved.
2) The external expansion interface is added to the universal monitoring equipment, the external expansion external sensor is externally connected, convenience is provided for the arrangement and expansion of the equipment under the conditions that the sensors need to be dispersedly arranged or a plurality of sensors need to be simultaneously accessed, other modules of the universal equipment are reused, the equipment purchase cost is reduced, and the universality of the equipment is improved.
3) The external expansion interface can also expand an external sound-light alarm to realize two modes of local autonomous sound-light alarm and cloud platform triggered sound-light alarm.
4) The power output control module is added, so that power supply to the built-in sensor, the external sensor and the wireless communication module can be cut off when necessary, and the energy-saving effect is achieved.
5) The data in a certain time period are collected through the built-in tilt angle sensor, the built-in stay wire displacement sensor and the built-in three-axis acceleration sensor of the field universal monitoring equipment, and are analyzed through the computing method and the process of the cloud platform, so that the adaptability of the universal monitoring equipment is improved, the more accurate comprehensive early warning judgment process is optimized and formed based on the computing method of the monitoring result value, the comprehensive early warning level professional degree of the deployed part of the universal monitoring equipment in a disaster area is more accurate, and early warning is more timely.
6) The probability and the severity of the occurrence of the geological disaster are continuously analyzed and predicted through data change, the configuration instruction is monitored and updated in real time, the early warning accuracy is greatly improved, the response speed of the geological disaster is improved to a great extent, and casualties and property loss in a disaster area are reduced.
7) The data visualization application platform is used for linking a plurality of users, so that the whole-process multi-user linkage interaction of alarm confirmation, release and alarm elimination is realized, the release and processing of the early warning information are more efficient, and the processing flow can be traced; the method realizes the association and visual presentation of monitoring data and GIS data such as a three-dimensional earth surface model, a digital elevation model, a laser point cloud model, a digital map, a satellite image and the like of a project, displays the position of a monitoring object where an early warning monitoring point is located, the geometric characteristic information of the early warning position of the monitoring object, the terrain reported by the early warning position and the road equal to emergency evacuation and transfers related geographic information to a user more intuitively and definitely, realizes the fusion and visual presentation of monitoring results in a geographic information system, shares the monitoring results in a network browser mode and the like, provides information for relevant early warning rescuers to respond in time according to a platform, strives for precious time for emergency rescue and disaster relief, and reduces casualties and property loss in disaster areas.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a diagram of a generic monitoring device architecture;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-2, the object of the present invention is achieved by the following technical solutions: a universal monitoring and early warning system comprises universal monitoring equipment and a universal monitoring cloud platform for geological disasters; and the universal monitoring equipment is in communication connection with the universal monitoring cloud platform for geological disasters. The universal monitoring device comprises a computing storage Module (MCU), a built-in sensor module, a power management module, a wireless communication module, a positioning module and an external expansion interface. The geological disaster pervasive monitoring cloud platform comprises an Internet of things equipment management system, a monitoring data analysis and early warning system, a database and a data visualization application platform, wherein the Internet of things equipment management system is connected with the monitoring data analysis and early warning system, the database and the data visualization application platform through data interfaces.
The universal monitoring equipment can be set to be 1-N according to needs, network communication is established between the universal monitoring equipment and the Internet of things equipment management system, the universal monitoring equipment is used for uploading monitored data to the Internet of things equipment management system after preliminary judgment, and meanwhile, instructions issued by the Internet of things equipment management system are received and executed.
The calculation storage Module (MCU) is responsible for logic calculation and data storage work, and performs operations such as data acquisition, analytical calculation, preliminary early warning judgment, data storage and the like by controlling the built-in sensor and the external sensor; and executing an internal program to control the work of the power supply management module, the wireless communication module and the positioning module.
The built-in sensor module also comprises a built-in tilt angle sensor module, a built-in stay wire displacement sensor module and a built-in triaxial accelerometer module; the built-in tilt angle sensor module comprises a three-axis tilt angle sensor and is responsible for collecting the inclination of deployment point positions of the universal monitoring equipment, wherein the inclination comprises inclination components of two orthogonal axes X/Y of an inclined plane and the tilt angle of a normal vector of the inclined plane relative to a horizontal plane. In practical application, the positive direction of the X, Y axis is respectively pointed to the inclination and the trend of a slope, and the change of the inclination angle of the Z axis after the inclination occurs represents the actual inclination angle of the measured object. The built-in stay wire displacement sensor module comprises a stay wire displacement sensor which is used for collecting the width change of the cracks of the hidden danger points of the ground disaster, the relative slippage of the side slope and the like, and is deployed along the direction perpendicular to the cracks or parallel to the main sliding direction of the side slope (generally the inclination of the side slope) during field deployment. The built-in triaxial accelerometer module comprises a triaxial acceleration sensor which is responsible for monitoring the acceleration of the object X, Y, Z in three directions.
The power management module comprises a lithium battery pack, a solar controller, a voltage detection module and a power output control, when the solar panel or external direct current is charged, the voltage is reduced through the power output control, the power is rectified, and an external input power is converted into proper voltage and current to charge the lithium battery pack; the lithium battery pack outputs power to all modules of the whole equipment through the power output control module, and the power output is controlled by the calculation storage Module (MCU), so that the power supply to the built-in and external sensors and the wireless communication module can be cut off when necessary, and the energy-saving effect is achieved.
The wireless communication module can adopt an NB-IoT or 4G communication module to realize data communication.
The positioning module is internally provided with a Beidou or GPS positioning module, comprises a built-in antenna, is powered on at regular time to wake up to obtain the position information of the equipment, and the position information of the equipment is used for equipment space positioning in a cloud platform visual system.
The external expansion interface is externally connected in a wired (RS485) or wireless (LoRa) mode, and multiple types of geological environment monitoring sensors such as a fixed inclinometer, a rain gauge, a soil moisture content meter, a static level gauge, a laser range finder, a mud level meter, an underground water level meter, a stay wire displacement, an inclination angle, temperature and humidity and a vibrating wire type sensor are expanded, and the external expansion interface is used for realizing real-time monitoring on geological conditions together with a built-in sensor module. The method and the device provide convenience for the deployment and expansion of the equipment, reduce the equipment purchase cost and improve the universality of the equipment.
The external expansion interface can be externally connected with an alarm device in a wired (RS485) or wireless (LoRa) mode, and an external audible and visual alarm can be expanded, so that two modes of local autonomous audible and visual alarm and cloud platform triggered audible and visual alarm are realized.
The Internet of things equipment management system is constructed on computer hardware, an operating system and an operating environment, can access resources such as a network and a database, establishes communication connection and equipment access management with field universal monitoring equipment through a network protocol, and specifically comprises the following processes:
1) data reception and parsing
After receiving the standard data frame reported by the ubiquitous monitoring equipment, the Internet of things equipment management system checks, analyzes and splits the data frame, and stores the data frame into a database in a classified manner, wherein the data is successfully received;
and after the data is successfully received, the Internet of things equipment management system returns a data receiving success message to the on-site universal monitoring equipment, and after the universal monitoring equipment receives the data receiving success message, the communication connection with the Internet of things equipment management system is disconnected. If not successful, the retransmission is carried out until the preset retransmission times are exceeded.
2) Device configuration and configuration update
Connecting the universal monitoring equipment and a computer by using a data line, configuring system access parameters for the universal monitoring equipment, and performing initial configuration;
after the initial configuration of the universal monitoring device is completed, a network communication connection (based on network protocols such as TCP, HTTP, MQTT, COAP and the like) is established with the Internet of things device management system through an internal communication module of the universal monitoring device, and at the moment, the configuration of the Internet of things device management system includes but is not limited to the following parameter configurations:
the power-on stable time length (unit: s) -the power-on stable time length before the universal monitoring equipment collects data is set, so that various sensors enter a power-on stable state during data collection;
and (3) recovering the waiting time and the re-collecting threshold value, namely setting the longest waiting time for the sensor to recover the data when the data of the universal monitoring equipment is collected, if the waiting time for recovering the data of a certain equipment exceeds the waiting time, sending a collecting instruction again, and if the repeated collecting instruction exceeds the re-collecting threshold value, judging that the equipment is in failure, and subsequently, not collecting the equipment any more.
Data acquisition cycle (unit: s) -set the data acquisition cycle of the universal monitoring equipment.
Data reporting period (unit: s) -setting a data reporting period of the universal monitoring equipment, starting the communication module and sending the latest data to the cloud platform by the universal monitoring equipment after each period starts, and then closing the communication module to save electricity.
Alarm configuration-setting the alarm address of the external interface extension of the universal monitoring equipment.
After the parameters are input, the Internet of things equipment management system stores the configuration parameter information into a database, processes the configuration information to form an equipment configuration standard data frame, and enters an instruction issuing queue;
and after the ubiquitous monitoring equipment establishes communication connection with the cloud platform in the next data reporting period, transmitting the equipment configuration standard data frame to the ubiquitous monitoring equipment by the issuing queue of the Internet of things equipment management system, and completing parameter configuration.
In the using process, if the parameters of the universal monitoring equipment need to be updated and configured, the response parameters are changed according to the process, the IOT equipment management system updates the response values in the database, meanwhile, equipment configuration standard data frames are generated, an instruction issuing queue is entered, and after the universal monitoring equipment establishes communication connection with the cloud platform in the next data reporting period, the issuing queue of the IOT equipment management system transmits the equipment configuration standard data frames to the universal monitoring equipment to complete parameter updating and configuration.
The monitoring data analysis and early warning system is constructed on computer hardware, an operating system and an operating environment, can access resources such as a network and a database, is connected with an Internet of things management platform through a data interface, and is responsible for monitoring data calculation processing, analysis and judgment, early warning triggering, early warning release and the like, and the specific flow is as follows:
1) monitoring data analysis and parameter update configuration
Before the system runs, monitoring items and monitoring point parameter information are configured, and then grading early warning thresholds of all monitoring result parameters are filled, wherein the grading early warning thresholds comprise I-IV grade early warning values, and whether a delay alarm function is started or not is configured;
then, the information monitoring data analysis and early warning system retrieves the monitoring data analyzed by the internet of things equipment management system from the database according to a time period and carries out operation processing, and a monitoring result value is calculated:
comparing a monitoring result value with a set configuration updating threshold value, issuing a new configuration instruction to an Internet of things equipment management system when the monitoring result value reaches the configuration updating threshold value, modifying parameter configuration, shortening parameters such as a data acquisition period, a data reporting period and a reply waiting time, increasing data acquisition frequency and enhancing data monitoring strength;
secondly, after the comprehensive early warning level after a certain round of monitoring data analysis and early warning calculation processing reaches an early warning state, the monitoring data analysis and early warning system sends a real-time acquisition uploading instruction to on-site universal monitoring equipment through the Internet of things equipment management system, parameters such as a data acquisition period, a data reporting period, a reply waiting time length and the like are modified into uninterrupted acquisition, after the universal monitoring equipment receives the instruction, the universal monitoring equipment is adjusted to a real-time acquisition and return mode of long power-on of a sensor and a communication module, and monitoring data are continuously returned to a cloud platform for processing.
2) Monitoring data analysis and early warning configuration
When the comprehensive early warning level after the analysis of the monitoring data and the early warning calculation processing reaches an early warning state (IV-I level) or the comprehensive early warning level is changed compared with the previous period (for example, the early warning level is improved), generating an early warning short message according to a preset format, and sending the early warning short message:
firstly, the information is pushed to the mobile phones of related managers through a mobile phone short message service provider, and the related managers master the site dynamics in time;
secondly, the vehicle-mounted GPS navigation system pushes the vehicle to a new vehicle in an early warning area to remind drivers and passengers of avoiding in advance;
meanwhile, an acousto-optic alarm instruction is sent to an acousto-optic alarm device on the site universal monitoring equipment through the Internet of things equipment management system to inform surrounding personnel of evacuating at a high speed so as to avoid casualties;
and fourthly, uploading the data to a data visualization application platform, triggering the platform to alarm, and facilitating further judgment of a platform administrator user.
Meanwhile, the system stores monitoring result data, early warning state, early warning short message and other information into the database, and once the next cycle is reached, the processing flow is continuously executed. And when the comprehensive early warning level of the next period is kept unchanged, generating no new early warning short message and pushing no early warning short message and audible and visual warning instruction.
The database stores an Internet of things equipment management system, a monitoring data analysis and early warning system, a data visualization application platform related configuration file and monitoring data.
The data visualization application platform realizes man-machine interaction operation and display based on monitoring and early warning data, and mainly comprises a data query and visualization unit, an early warning release unit, an early warning and alarm elimination processing unit, a data information superposition GIS map display unit, a data information superposition three-dimensional model display unit and a data statistics unit, wherein the data query and visualization, the early warning release, the early warning and alarm elimination processing flow, the data information superposition GIS map display, the data information superposition three-dimensional model display and the data statistics are realized by the data statistics unit, and the specific flow is as follows:
1) data query statistics and visualization
The data visualization application platform calls processed data from the database according to a preset rule to perform secondary statistical processing such as calculating the arithmetic mean, standard deviation, filtering, summarizing according to time periods such as days and hours, automatically drawing the calculation result (producing various graphs such as a broken line graph, a scatter diagram, a rose diagram and a pie diagram), and displaying the data and the drawing result through a browser.
2) Early warning issuing, early warning and alarm eliminating processing
The monitoring data analysis and early warning system monitors data batches reaching or exceeding an early warning threshold value, triggers an alarm, and firstly pushes early warning information and platform display to an account of a platform administrator user;
the data visualization application platform marks out early warning data with corresponding ground color according to a preset early warning level, simultaneously triggers the monitoring point positioning icon on the GIS to be converted into a corresponding early warning color block and twinkle, and after receiving an early warning prompt, a platform administrator user checks the early warning data and checks the condition of equipment, confirms the warning condition:
if the alarm is true, the platform administrator user marks the true alarm in the platform, and the data visualization platform pushes the information and the data of the true alarm to the account of the engineering related party personnel to realize the release of the alarm;
if the alarm is false alarm, the platform administrator user marks the alarm as false alarm in the platform, the data visualization platform closes the early warning state, returns to the normal monitoring mode, and does not issue the alarm to the personnel of the engineering relevant party;
after receiving the early warning information, the management personnel of the engineering related party further confirms the alarm condition in the data visualization management platform, and if the alarm condition is real, the data visualization management platform calls an emergency plan from the database for the management personnel of the engineering related party to use for reference; and if the management personnel of the relevant engineering party judge that the alarm is false, the data visualization management platform closes the early warning state and returns to the normal monitoring mode.
The invention realizes the whole process of multi-user linkage interaction of alarm confirmation, release and alarm elimination by linking a plurality of users through the platform, the release and processing of the early warning information are more efficient, and the processing flow can be traced.
And if the platform administrator user needs to change the data monitoring frequency and period, issuing a configuration updating instruction, receiving the instruction by the Internet of things equipment management system, sending the instruction to the on-site universal monitoring equipment, and executing updating configuration by the on-site universal monitoring equipment.
3) Data information superposition GIS map display and data information superposition three-dimensional model display
The information superposition GIS map display unit comprises a project terrain data module, a digital elevation model, a digital map data module, a satellite image data module, a project earth surface inclined photography data module, an earth surface three-dimensional laser point cloud data module and the like, and is used for storing data such as project terrain data, the digital elevation model, the digital map data, the satellite image data, project earth surface inclined photography data, earth surface three-dimensional laser point cloud data and the like, the data are positioned and superposed on the three-dimensional model according to GPS coordinates reported by a universal monitoring device and presented as virtual monitoring points, and the virtual monitoring points are associated with corresponding monitoring data of the points in a database. And after the platform administrator user clicks the virtual monitoring point, the virtual monitoring point displays corresponding monitoring result data and a time curve in a small popup window mode, and interaction of a user side browser and a server side data visualization platform is realized through an HTTP protocol.
The monitoring and early warning result is more visualized, aggregated and information-sharing interactive platform by means of sharing through a network browser and the like, and the optimization decision of a manager is facilitated.
A method based on a general monitoring and early warning system for geological disasters comprises the following specific working steps:
step one (S1): the method comprises the steps that a universal monitoring device reads configuration information of an Internet of things device management system, and acquires original data to upload to a cloud platform according to configuration requirements;
wherein, the specific process of S1 is as follows:
s1.1, starting a period, receiving a configuration instruction sent by an Internet of things management system by a universal monitoring device to set configuration information, electrifying an internal sensor and an external interface, acquiring data after the electrifying stability time is reached, and initially starting threshold judgment:
s1.2, if the threshold value exceeds a preset threshold value, starting a power supply of the wireless communication module, establishing network communication with an Internet of things management system, and sending monitoring data;
s1.3, if the current period does not exceed a preset threshold, comparing whether the current period is a data uploading period, and if the current period is the data uploading period, turning on a power supply of the wireless communication module and sending monitoring data to the cloud platform; if the data is not in the data uploading period, after a data receiving success instruction replied by the cloud platform is received, the power supply of the sensor and the external interface is cut off, and the cloud platform enters a dormant state;
s1.4, if the cloud platform replies a configuration updating instruction or an early warning instruction after receiving the data, applying configuration updating or triggering an acousto-optic alarm module of an external interface, and then entering a dormant state.
S1.5, starting the next period, applying the latest configuration information to carry out data acquisition, and executing the steps S1.1-S1.4.
Step two (S2): the Internet of things equipment management system checks, analyzes and splits the data of the universal monitoring equipment, stores the data into the database in a classified manner, and receives the instruction from the database and sends the instruction to the universal monitoring equipment;
step three (S3): after the monitoring data analysis and early warning system receives the data in the database, the monitoring result value is calculated,
when the monitoring result value reaches a configuration updating threshold value, a configuration updating instruction is issued to the Internet of things equipment management system, parameter configuration is modified, and parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like are shortened;
comparing the monitoring result value with a grading early warning threshold value, carrying out comprehensive early warning, and issuing a data receiving success instruction when the result value does not reach the comprehensive early warning grade;
when the comprehensive early warning level reaches an early warning state, sending a configuration updating instruction, modifying parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like into uninterrupted acquisition, and acquiring and returning in real time by adaptive monitoring equipment;
when the comprehensive early warning level reaches an early warning state or the comprehensive early warning level is changed compared with the previous period, triggering an early warning instruction, generating an early warning short message according to a preset format, and sending the early warning short message to related personnel; meanwhile, the monitoring data analysis and early warning system stores all information into a database;
according to the method, after the comparison with the configuration updating threshold value is carried out, the configuration updating is carried out, the data acquisition period and the reporting flow are shortened, a large amount of data are used for monitoring different stages of the geological disaster inoculation process, the comprehensive early warning grade prejudgment is combined, and the big data are comprehensively utilized for analyzing and monitoring the geological disaster in real time, so that precious time is won for field emergency rescue, and the big data are obtained and provide big data support for prevention, control and research of the geological disaster.
Step three (S3) relates to a method for calculating a monitoring result value, specifically:
1) inclination angle monitoring data processing and threshold value comparison process
Reading the original inclination angle monitoring data from a database:
θx-an X angle (°) measured by the tilt sensor;
θy-the Y angle (°) measured by the tilt sensor;
second, calculate the total inclination angle theta of the sensorz：
Figure RE-GDA0003050223060000131
And thirdly, calculating the change of the inclination angle:
subtracting the previous period measurement value from the current measurement value of the inclination angles of the x, y and z three axes to obtain the current inclination angle variation;
Δθ(i)＝θ(i)-θ(i-1)
fourthly, calculating the accumulated inclination angle change:
subtracting the measurement value at initial zero setting from the current measurement value of the inclination angles of the x, y and z three axes to obtain the accumulated variation of the inclination angles;
∑θ(i)＝θ(i)-θ(0)
calculating the daily cumulative change rate of the inclination angle on the day:
subtracting the measurement value of the last acquisition period of the previous day of the axis from the measurement value of the inclination angle of the x, y and z axes to obtain the current accumulated variation;
recording the first acquisition period of the day as T1The last acquisition period of the previous day is T0The inclination angle of the X changes in the same day:
Δθ(T1)＝∑θ(T1)-∑θ(T0)
Δθ(Ti)＝∑θ(Ti)-∑θ(Ti-1)+Δθ(Ti-1)
based on total inclination angle theta of sensorzCalculating the accumulated relative displacement of the dangerous rock mass, the boulder or the shallow landslide:
accumulating the horizontal displacement:
∑H(i)＝L*sinθz(i)-L*sinθz(0)
accumulating the vertical displacement:
∑V(i)＝L*cosθz(i)-L*cosθz(0)
wherein, L represents the rigid body characteristic length of the dangerous rock mass and the boulder;
when the method is used for shallow slope measurement, L represents the length of the steel pipe driven below the slope surface;
and seventh, warning and judging: respectively comparing the calculation results with preset grading early warning threshold values, wherein early warning grades are classified from high to low by I-IV grades; after comparison, endowing each monitoring result parameter with a corresponding early warning level; finally, taking the early warning level with higher grade as the early warning level of the monitoring point;
after early warning judgment is finished, storing the calculation result of the monitoring data and early warning level information into a database;
at present, most of general monitoring devices adopt gyroscope type tilt sensors for tilt angle monitoring, and are limited by the fact that accumulated errors are easily generated by the principle of measuring tilt angles by a gyroscope, so that the current most of general monitoring devices are low in tilt angle measurement precision and cannot measure small tilt angle changes of geological disaster bodies (landslides, collapses and the like). In addition, other similar devices only calculate the component of the inclination angle and do not calculate the total inclination angle, so that the accumulated inclination change of the main direction of the landslide or the dangerous rock mass cannot be judged.
The method adopts the inclination angle sensor based on the acceleration principle, belongs to the state quantity, has no accumulated error and has higher precision. And a calculation method for calculating the total inclination angle (Z axis) of the sensor based on X, Y two axes is added, and by introducing a characteristic length parameter L, the horizontal displacement and the vertical displacement of multiple geological disaster types such as dangerous rock masses, boulders, shallow landslides and the like can be calculated based on the inclination angle, and early warning comparison and evaluation are carried out (the parameters are important reference factors for judging the early warning level of the geological disaster bodies). The method greatly expands the use scenes of the universal monitoring equipment and improves the effectiveness and reliability of early warning evaluation.
2) Stay wire displacement data processing and threshold value comparison process
Reading original data of stay wire displacement monitoring from a database: stay length measurement S (mm)
Secondly, calculating the displacement: the measured value of the length of the stay wire in the last period is subtracted from the measured value of the length of the stay wire in the current period, and the formula is as follows
ΔS(i)＝S(i)-S(i-1)
Calculating the accumulated displacement: the measured value of the stay length at the initial zero setting is subtracted from the measured value of the stay length at this time, and the formula is as follows
∑S(i)＝S(i)-S(0)
Fourthly, calculating daily change rate of the displacement on the day:
recording the first acquisition period of the day as t1The last collection period of the previous day is t0Cumulative shift (mm) of the day:
ΔS(t1)＝∑S(t1)+∑S(t0)
ΔS(ti)＝∑S(ti)-∑S(ti-1)+ΔS(ti-1)
calculating the accumulated horizontal displacement and the accumulated vertical displacement by combining the monitoring data of the sensor oblique angle theta
Accumulating the horizontal displacement:
∑H(i)＝∑S(i)*sinθ
accumulating the vertical displacement:
∑V(i)＝∑S(i)*cosθ
wherein theta represents an inclination angle measurement value parallel to the stretching direction of the stay wire displacement sensor;
or selecting the theta of the tilt sensor according to the deployment orientation of the general monitoring equipmentxOr thetayAs θ;
sixthly, early warning judgment: respectively comparing the calculation results with a preset grading early warning threshold value, wherein the early warning grades are classified from high to low by I-IV, after comparison, giving the corresponding early warning grade to each monitoring result parameter, and finally taking the early warning grade with a higher grade as the early warning grade of the monitoring point;
seventhly, after the early warning judgment is finished, the monitoring data calculation result and the early warning level information are stored in a database;
the stay wire displacement monitoring data is generally used for calculating the crack width of a landslide, most of current general monitoring equipment and systems do not have a multi-source sensor data coupling calculation function, only data such as an inclination angle, the stay wire crack width and acceleration are calculated independently, and single parameters are compared independently for early warning. The method can calculate the conventional current change and accumulated change, increases the daily change rate, the horizontal displacement and the vertical displacement of the day, expands the monitoring method adaptability of the universal monitoring equipment, and can monitor the horizontal and vertical displacements of the boulder, the dangerous rock body and the landslide body by utilizing the stay wire displacement sensor arranged in the monitoring equipment and perform multi-parameter early warning judgment and early warning.
3) Acceleration data processing and threshold comparison process
Acceleration data sampling: the acquisition of acceleration period data can be controlled by controlling the electrifying time, the time for once electrifying acquisition can be set, the acquisition interval is the sampling frequency, and the data is transmitted to a cloud platform database for storage after the data transmission period is reached; the time length of one-time power-on collection can be set to be 120s, the collection interval is 1 h/time, the sampling frequency is 100Hz, and the method can also be flexibly set according to the requirement of a configuration updating instruction.
Secondly, reading time-domain acceleration time-domain data of a time period from a database by a monitoring data analysis and early warning system process, converting the time-domain data of the acceleration into frequency-domain data after the time-domain data is processed by an integral and Fourier transform algorithm, and obtaining frequency spectrum data with the abscissa as frequency/Hz and the ordinate as amplitude;
thirdly, the natural vibration frequency perpendicular to the sliding surface direction of the dangerous rock mass is represented by extracting the maximum amplitude and the corresponding frequency in the frequency spectrum data, and the maximum amplitudes of the two adjacent vibrations are respectively recorded as A1、A2Corresponding frequencies are respectively fd(t1)、fd(t2) The corresponding moments of the two vibrations are t1、t2And calculating the damping ratio xi and the undamped vibration frequency f in the direction vertical to the sliding surface of the dangerous rock mass according to the following formula:
Figure RE-GDA0003050223060000161
Figure RE-GDA0003050223060000162
fourthly, the calculated undamped vibration frequency f represents the undamped self-vibration frequency of the monitored dangerous rock mass, the bonding area of the dangerous rock mass and the bedrock is gradually reduced along with the development of the cracks of the dangerous rock mass, the self-vibration frequency f is gradually reduced, when f is lower than a critical value, the dangerous rock collapses, the calculated result f is compared with a preset grading early warning threshold value, the early warning grade is classified from high to low into I-IV grade, and after comparison, the monitoring result parameters are endowed with corresponding early warning grades;
and fifthly, storing the calculation result of the monitoring data and the early warning level information into a database after the early warning is judged.
The method acquires acceleration data within a certain period of time through an acceleration sensor built in site universality monitoring equipment, performs time domain-frequency domain conversion and vibration analysis based on a frequency domain through a computing method and a computing process of a cloud platform, really achieves effective and reliable early warning based on the acceleration data, is particularly suitable for monitoring and early warning of high-sudden geological disaster types such as slipping type and falling type dangerous rock masses of a weak damping system, and is superior to the optimal early warning period based on displacement monitoring in sensitivity and the optimal early warning period.
Step four (S4): the universal monitoring equipment modifies the new configuration parameters according to the instruction issued by the equipment management system of the internet of things and executes the step S1 according to the instruction;
step five (S5): the data visualization application platform triggers the monitoring point positioning icon identification early warning color block on the GIS and flickers according to the early warning information;
step six (S6): after receiving the early warning prompt, the platform user checks the early warning data, checks the equipment condition and confirms whether the alarm condition is a real dangerous condition; if the alarm condition is a real alarm condition, the engineering manager user marks the real alarm condition in the platform, and the data visualization platform pushes the information and the data of the real alarm condition to the account number of the engineering related party personnel to realize the release of the alarm condition; if the alarm is false alarm, the engineering manager user marks the alarm as false alarm in the platform, the data visualization platform closes the early warning state, returns to the normal monitoring mode, and does not issue the alarm to the personnel of the engineering related party; after receiving the early warning information, the management personnel of the engineering related party further confirms the alarm condition in the data visualization management platform, and if the alarm condition is real, the data visualization management platform calls an emergency plan from the database for the management personnel of the engineering related party to use for reference; if the management personnel of the relevant engineering party judge that the alarm is false, the data visualization management platform closes the early warning state and returns to the normal monitoring mode;
step seven (S7): and the platform administrator confirms that the data monitoring frequency and period need to be changed, a configuration updating instruction is issued to the Internet of things equipment management system, and the universal monitoring equipment executes the step S4.
The invention carries out comprehensive calculation and judgment according to the early warning levels of a plurality of built-in and external sensor monitoring results of the universal monitoring equipment, optimizes and forms a more accurate comprehensive early warning judgment process, and comprises the following steps:
1) the monitoring data analysis and early warning system reads the monitoring data calculation results and early warning level information of the three built-in sensors from a database;
2) and (4) executing judgment:
firstly, when one of the three sensor monitoring data achievements gives an early warning (the early warning level is more than or equal to 0), the early warning level of the sensor is taken as the comprehensive early warning level of the deployment position of the monitoring equipment.
And secondly, when two sensors have early warning in monitoring data results (the early warning level is more than or equal to 0), the higher level is defined as the comprehensive early warning level of the deployment position of the monitoring equipment.
And thirdly, when the monitoring data of three or more (including external sensors) sensors has early warning, taking the highest early warning level as a reference, and then increasing an early warning level (the highest level is I) to be classified as the comprehensive early warning level of the deployment position of the monitoring equipment.
Fourthly, after the judgment is finished, outputting the comprehensive early warning result, and storing the result into a database.
Compared with the existing comprehensive early warning judgment method, the method has the advantages of more accurate calculation, more comprehensive monitoring range and more reliable data, and greatly improves the early warning accuracy.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (11)

1. The utility model provides a general type of geological disasters monitors early warning system which characterized in that: the universal monitoring and early warning system comprises universal monitoring equipment and a universal monitoring cloud platform for geological disasters;
the universal monitoring equipment is in communication connection with the universal monitoring cloud platform for the geological disasters; the universal monitoring equipment comprises a computing storage module, a built-in sensor module, a power management module, a wireless communication module, a positioning module and an external expansion interface; the built-in sensor module also comprises a built-in tilt angle sensor module, a built-in stay wire displacement sensor module and a built-in triaxial accelerometer module;
the geological disaster pervasive monitoring cloud platform comprises an Internet of things equipment management system, a monitoring data analysis and early warning system, a database and a data visualization application platform, wherein the Internet of things equipment management system, the monitoring data analysis and early warning system, the database and the data visualization application platform are connected through data interfaces; the universal monitoring equipment is used for uploading monitored data to the Internet of things equipment management system and receiving and executing instructions issued by the Internet of things equipment management system; the Internet of things equipment management system analyzes the received data and stores the analyzed data into a database, and a configuration updating file input in a monitoring data analysis and early warning system or a data visualization application platform is issued to a universal monitoring device in a command form for configuration updating; the monitoring data analysis and early warning system is responsible for monitoring data calculation processing, analysis and judgment, early warning configuration, early warning triggering, early warning release and storing data into a database; the database stores relevant data of a universal monitoring device, an Internet of things device management system, a monitoring data analysis and early warning system and a data visualization application platform; the data visualization application platform is used for realizing man-machine interactive operation.
2. The general type of geological disaster monitoring and early warning system of claim 1, characterized in that: the universal monitoring device further comprises an external charging interface.
3. The general type of geological disaster monitoring and early warning system of claim 1, characterized in that: the power management module comprises a lithium battery pack, a solar controller, a voltage detection module and power output control.
4. The general type of geological disaster monitoring and early warning system of claim 1, characterized in that: the external expansion interface is connected with an alarm device.
5. The general type of geological disaster monitoring and early warning system of claim 1, characterized in that: the external expansion interface is connected with an external sensor.
6. The general type of geological disaster monitoring and early warning system of claim 5, wherein: the external sensor comprises a fixed inclinometer, a rain gauge, a soil moisture content meter, a static level gauge, a laser range finder, a mud level meter, an underground water level meter, a stay wire displacement sensor, an inclination angle sensor, a temperature sensor, a humidity sensor and a vibrating wire sensor.
7. The general type of geological disaster monitoring and early warning system of claim 1, characterized in that: the data visualization application platform comprises a data query and visualization unit, an early warning release unit, an early warning and alarm elimination processing unit, a data information superposition GIS map display unit, a data information superposition three-dimensional model display unit and a data statistics unit.
8. The system of claim 7, wherein the system comprises: the early warning issuing unit establishes network communication with a vehicle-mounted GPS navigation system, and the vehicle-mounted GPS navigation system receives early warning information sent by the early warning issuing unit.
9. A method based on a general monitoring and early warning system for geological disasters comprises the following specific working steps:
s1: the method comprises the steps that a universal monitoring device reads configuration information of an Internet of things device management system, configures parameters according to requirements, collects original data and uploads the original data to a cloud platform;
s2: the Internet of things equipment management system checks, analyzes and splits the data of the universal monitoring equipment, stores the data into the database in a classified manner, and receives the instruction from the database and sends the instruction to the universal monitoring equipment;
s3: after the monitoring data analysis and early warning system receives the data in the database, calculating a monitoring result value, when the monitoring result value reaches a configuration updating threshold value, issuing a configuration updating instruction to the Internet of things equipment management system, modifying parameter configuration, and shortening parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like;
comparing the monitoring result value with a grading early warning threshold value, carrying out comprehensive early warning, and issuing a data receiving success instruction when the result value does not reach the comprehensive early warning grade; when the comprehensive early warning level reaches an early warning state, the monitoring data analysis and early warning system sends a configuration updating instruction, parameters such as a data acquisition period, a data reporting period, a reply waiting time and the like are modified into uninterrupted acquisition, and the universal monitoring equipment acquires return data in real time; when the comprehensive early warning level reaches an early warning state or the comprehensive early warning level is changed compared with the previous period, sending a configuration updating instruction, generating an early warning short message according to a preset format and sending the early warning short message to related personnel;
meanwhile, the monitoring data analysis and early warning system stores all information into a database;
s4: the universal monitoring equipment modifies the new configuration parameters according to the instruction issued by the equipment management system of the internet of things and executes the step S1 according to the instruction;
s5: the data visualization application platform triggers the monitoring point positioning icon identification early warning color block on the GIS and flickers according to the early warning information;
s6: after receiving the early warning prompt, the platform administrator user checks the early warning data and checks the equipment condition, and then confirms whether the alarm condition is a real dangerous condition or not; if the alarm condition is a real alarm condition, the engineering manager user marks the real alarm condition in the platform, and the data visualization platform pushes the information and the data of the real alarm condition to the account number of the engineering related party personnel to realize the release of the alarm condition; if the alarm is false alarm, the engineering manager user marks the alarm as false alarm in the platform, the data visualization platform closes the early warning state, returns to the normal monitoring mode, and does not issue the alarm to the personnel of the engineering related party; after receiving the early warning information, the management personnel of the engineering related party further confirms the alarm condition in the data visualization management platform, and if the alarm condition is real, the data visualization management platform calls an emergency plan from the database for the management personnel of the engineering related party to use for reference; if the management personnel of the relevant engineering party judge that the alarm is false, the data visualization management platform closes the early warning state and returns to the normal monitoring mode;
s7: and the platform administrator confirms that the data monitoring frequency and period need to be changed, a configuration updating instruction is issued to the Internet of things equipment management system, and the universal monitoring equipment executes the step S4.
10. The method for monitoring and warning the general type of geological disasters according to claim 9, wherein the step S1 comprises the following steps:
s1.1, starting a period, receiving a configuration instruction sent by an Internet of things management system by a universal monitoring device to set configuration information, electrifying an internal sensor and an external interface, acquiring data after the electrifying stability time is reached, and initially starting threshold judgment:
s1.2, if the threshold value exceeds a preset threshold value, starting a power supply of the wireless communication module, establishing network communication with an Internet of things management system, and sending monitoring data;
s1.3, if the current period does not exceed a preset threshold, comparing whether the current period is a data uploading period, and if the current period is the data uploading period, turning on a power supply of the wireless communication module and sending monitoring data to the cloud platform; if the data is not in the data uploading period, after a data receiving success instruction replied by the cloud platform is received, the power supply of the sensor and the external interface is cut off, and the cloud platform enters a dormant state;
s1.4, if the cloud platform replies a configuration updating instruction or an early warning instruction after receiving the data, applying configuration updating or triggering an acousto-optic alarm module of an external interface, and then entering a dormant state.
S1.5, starting the next period, applying the latest configuration information to carry out data acquisition, and executing the steps S1.1-S1.4.
11. The method for monitoring and warning the system based on the pervasive type of geological disaster as claimed in claim 9, wherein the monitoring result value calculating method in step S3 is as follows:
1) inclination angle monitoring data processing and threshold value comparison process
Reading the original inclination angle monitoring data from a database:
θx-an X angle (°) measured by the tilt sensor;
θy-the Y angle (°) measured by the tilt sensor;
second, calculate the total inclination angle theta of the sensorz：
Figure FDA0002872641660000031
And thirdly, calculating the change of the inclination angle:
subtracting the previous period measurement value from the current measurement value of the inclination angles of the x, y and z three axes to obtain the current inclination angle variation;
Δθ(i)＝θ(i)-θ(i-1)
fourthly, calculating the accumulated inclination angle change:
subtracting the measurement value at initial zero setting from the current measurement value of the inclination angles of the x, y and z three axes to obtain the accumulated variation of the inclination angles;
∑θ(i)＝θ(i)-θ (0)
calculating the daily cumulative change rate of the inclination angle on the day:
subtracting the measurement value of the last acquisition period of the previous day of the axis from the measurement value of the inclination angle of the x, y and z axes to obtain the current accumulated variation;
recording the first acquisition period of the day as T1The last acquisition period of the previous day is T0The inclination angle of the X changes in the same day:
Δθ(T1)＝∑θ(T1)-∑θ(T0)
Δθ(Ti)＝∑θ(Ti)-∑θ(Ti-1)+Δθ(Ti-1)
based on total inclination angle theta of sensorzCalculating the accumulated relative displacement of the dangerous rock mass, the boulder or the shallow landslide:
accumulating the horizontal displacement:
∑H(i)＝L*sinθz(i)-L*sinθz(0)
accumulating the vertical displacement:
∑V(i)＝L*cosθz(i)-L*cosθz(0)
wherein, L represents the rigid body characteristic length of the dangerous rock mass and the boulder;
when the method is used for shallow slope measurement, L represents the length of the steel pipe driven below the slope surface;
and seventh, warning and judging: respectively comparing the calculation results with preset grading early warning threshold values, wherein early warning grades are I-IV grades from high to low; after comparison, endowing each monitoring result parameter with a corresponding early warning level; finally, taking the early warning level with higher grade as the early warning level of the monitoring point;
after early warning judgment is finished, storing the calculation result of the monitoring data and early warning level information into a database;
2) stay wire displacement data processing and threshold value comparison process
Reading original data of stay wire displacement monitoring from a database: stay length measurement S (mm)
Secondly, calculating the displacement: the measured value of the length of the stay wire in the last period is subtracted from the measured value of the length of the stay wire in the current period, and the formula is as follows
ΔS(i)＝S(i)-S(i-1)
Calculating the accumulated displacement: the measured value of the stay length at the initial zero setting is subtracted from the measured value of the stay length at this time, and the formula is as follows
∑S(i)＝S(i)-S(0)
Fourthly, calculating the daily change rate of the day:
recording the first acquisition period of the day as t1The last collection period of the previous day is t0Cumulative shift (mm) of the day:
ΔS(t1)＝∑S(t1)-∑S(t0)
ΔS(ti)＝∑S(ti)-∑S(ti-1)+ΔS(ti-1)
calculating the accumulated horizontal displacement and the accumulated vertical displacement by combining the monitoring data of the sensor oblique angle theta
Accumulating the horizontal displacement:
∑H(i)＝∑S(i)*sinθ
accumulating the vertical displacement:
∑V(i)＝∑S(i)*cosθ
wherein theta represents an inclination angle measurement value parallel to the stretching direction of the stay wire displacement sensor;
or selecting the theta of the tilt sensor according to the deployment orientation of the general monitoring equipmentxOr thetayAs θ;
sixthly, early warning judgment: respectively comparing the calculation results with preset grading early warning threshold values, wherein the early warning grades are classified from high to low into I-IV grades, after comparison, giving the corresponding early warning grade to each monitoring result parameter, and finally taking the early warning grade with a higher grade as the early warning grade of the monitoring point;
seventhly, after the early warning judgment is finished, the monitoring data calculation result and the early warning level information are stored in a database;
3) acceleration data processing and threshold comparison process
Acceleration data sampling: the acquisition of acceleration period data can be controlled by controlling the power-on time, the time for once power-on acquisition, the acquisition interval and the sampling frequency can be set, and the data are transmitted to a cloud platform database for storage after the data reach a data transmission period;
secondly, reading time-domain acceleration time-domain data of a time period from a database by a monitoring data analysis and early warning system process, converting the time-domain data of the acceleration into frequency-domain data after the time-domain data is processed by an integral and Fourier transform algorithm, and obtaining frequency spectrum data with the abscissa as frequency/Hz and the ordinate as amplitude;
thirdly, the natural vibration frequency perpendicular to the sliding surface direction of the dangerous rock mass is represented by extracting the maximum amplitude and the corresponding frequency in the frequency spectrum data, and the maximum amplitudes of the two adjacent vibrations are respectively recorded as A1、A2Corresponding frequencies are respectively fd(t1)、fd(t2) The corresponding moments of the two vibrations are t1、t2The damping ratio xi and the direction vertical to the sliding surface of the dangerous rock mass are calculated according to the following formulaDamping vibration frequency f:
Figure FDA0002872641660000061
Figure FDA0002872641660000062
fourthly, the calculated undamped vibration frequency f represents the undamped self-vibration frequency of the monitored dangerous rock mass, the bonding area of the dangerous rock mass and the bedrock is gradually reduced along with the development of the cracks of the dangerous rock mass, the self-vibration frequency f is gradually reduced, when f is lower than a critical value, the dangerous rock collapses, the calculated result f is compared with a preset grading early warning threshold value, the early warning grade is classified from high to low into I-IV grade, and after comparison, the monitoring result parameters are endowed with corresponding early warning grades;
and fifthly, after the early warning judgment is finished, storing the calculation result of the monitoring data and the early warning level information into a database.
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