LU501229B1 - Integrated control system and method of geological disaster prevention information service based on big data - Google Patents
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Abstract
An integrated control system and method of geological disaster prevention and control information service based on big data. The integrated control system of geological disaster prevention and control information service based on big data comprises a rainfall monitor module, a mud level monitoring module, a vibration monitoring module, a central control module, a wireless communication module, a cloud server, a smartphone, a disaster identification module, a risk assessment module, an alarm module and a display module. According to the invention, through the disaster identification module, the 3S technology, that is, GPS (Global Positioning System), RS (Remote Sensing) and GIS (Geographic Information System), is combined to locate and analyze the region where geological disasters occur, and finally the scale and harm degree of geological disasters are identified. At the same time, through the risk assessment module, geological disasters can be prevented in advance.
Description
DESCRIPTION LU501229 Integrated control system and method of geological disaster prevention information service based on big data
TECHNICAL FIELD The invention belongs to that technical field of geological disaster prevention and control, and particularly relates to an integrated control system and method of geological disaster prevention and control information service base on big data.
BACKGROUND Geological disasters refer to disastrous geological events formed by various geological processes during the development and evolution of the earth. The distribution and change law of geological disasters in time and space is not only controlled by the natural environment, but also related to human activities, which is often the result of the interaction between human beings and nature. Natural disasters mainly caused by geological dynamic activities or abnormal changes of geological environment. Under the action of internal power, external power or man-made geological power, abnormal energy release, material movement, deformation and displacement of rock and soil bodies and abnormal changes of environment occur on the earth, which endanger human life and property, life and economic activities or destroy resources and environment on which human beings depend for survival and development. Bad geological phenomena, usually called geological disasters, refer to geological events that caused by natural geological processes and human activities to deteriorate the geological environment, reduce the environmental quality, directly or indirectly endanger human safety and cause losses to social and economic construction. Geological disaster refers to the geological action (phenomenon) which is formed under the action of natural or human factors and causes damage and loss to human life, property and environment. Such as collapse, landslide, debris flow, ground fissure, land subsidence, ground collapse, rock burst, water inrush from tunnels, mud outburst, gas outburst, spontaneous combustion of coal seams, loess collapsibility, rock and soil expansion, sand liquefaction, land freeze-thaw, soil erosion, land desertification and swamping, soil salinization, and earthquake, volcano, geothermal hazards, etc. However, the current situation of geological disasters cannot be fully and quickly investigated; at the same time, the existing assessment of geological disasters is based on the grid unit superposition of the influencing factors of the formation of geological disasters, while ignoring the different movement characteristics of the formation and destruction mechanisms of different types of geological 501229 disasters, and the obtained assessment results cannot be directly applied to large-scale risk assessment management.
To sum up, the existing problems in the prior art are: (1) The existing geological disaster situation cannot be fully and quickly investigated; at the same time, the existing assessment of geological disasters is based on the grid unit superposition of the influencing factors of the formation of geological disasters, while ignoring the different movement characteristics of the formation and destruction mechanisms of different types of geological disasters, and the obtained assessment results cannot be directly applied to large-scale risk assessment management.
(2) In the process of real-time monitoring of ground motion data information by vibration sensors, the vibration sensors are easily disturbed, and the detection velocity and stability are relatively low with traditional algorithms.
(3) In the process of real-time monitoring rainfall data information by rainfall sensor, the current algorithm can't make the rainfall sensor have better input and output, resulting in low measurement accuracy.
(4) In the process of real-time monitoring the height data information of the sludge layer at the bottom of the hillside ditch, the thickness sensor is easily affected by the temperature, which leads to the decrease of the sensitivity coefficient, thus increasing the measurement error.
SUMMARY Aiming at the problems existing in the prior art, the invention provides an integrated control system and method of geological disaster prevention and control information service based on big data.
The invention is realized as follows: a method for integrated control of geological disaster prevention and control information service based on big data includes: step 1, collecting rainfall data information, height data information of sludge layer at the bottom of hillside ditch and ground motion data information; step 2, analyzing the above data signals and conducting risk assessment. When the risk assessment results reach a certain level, it is identified that disasters will occur; step 3, when it is recognized that a disaster will occur, the information is sent to the cloud server and smartphone to provide people with corresponding judgment results and give an alarm through the alarm; LU501229 step 4, the detected data and the corresponding judgment result are displayed on the display screen.
Further, the rainfall data information is monitored in real time by a rainfall sensor, and a sensor nonlinear dynamic compensation algorithm based on least square support vector machine is adopted, which comprises the following steps: step 1, dynamically testing the sensor to measure the input signal u(t) and the output response y(t) of the sensor; at the same time, the ideal (expected) response model 1s established and the signal u(t) is input to obtain the ideal response u' (t); step 2, in the “= on formula, it is the intermediate model parameter:
TT sample data: sides Te edd conn {bent ay eb oy Sead eo Pitié BY the expected compensation output u'(t), the measured output signal y(t), organize the training sample set {n(t),u(t)} and substitute it into the formula: seme ete oy LER get the intermediate model parameter X and get the intermediate model parameter ©; step 3, refer to BR A or DAS ; wherein ABA EB , Benth wl * and a he LLL BAD CH ; wherein Crisis tal 2 according to the intermediate parameter vector © and the corresponding elements, the matrices ¢ and A are constructed, and the parameter vector C and the linear dynamic link parameter vector B of the compensator are obtained by substituting the PO=CA'O=>C= 20 B--4 org formula © À, doo step 4, the mathematical analytical expression of compensator model is given through the identified nonlinear compensator parameter vectors A, B and C, and the nonlinear dynamic compensator is constructed.
Further, the height data information of the sludge layer at the bottom of the hillside ditch is monitored in real time by a thickness sensor, and the particle swarm optimization neural network algorithm is adopted, and the specific process is as follows: LU501229 firstly, the particle swarm optimization algorithm randomly initializes a particle swarm, and the characteristics of each particle are represented by three indexes of fitness value, position and velocity, and the optimal solution is found through iteration; in a D-dimensional search space, the population consisting of n particles is X = (Xi, Xo, ..., Xn), each particle i represents a D-dimensional vector, and Xi =[xi1, Xi, ..., Xin], represents the position of the particle in the D-dimensional search space; the fitness value corresponding to the position X; of each particle calculated according to the objective function can be expressed in the form of standard deviation, mean square deviation or variance; the velocity of the ith particle is Vi =[Vi, Vi, ..., Vin], and its individual extremum is Pi =[Pi, Pi, ..., Pin]; the global extremum of the race is Ps =[Pg1, Ps, ... Pep]; the formula for particles to update their velocity and position through individual extremum and population extremum is as follows: Vill = wViy + er, (Py, = X) + er, (Ph, — Xe) y _ x" + Vv +1 id id id : where © is the inertia weight; d=1, 2,....D; 1—1,2...,n; k is the velocity of the particle for the contemporary iteration number Vig; cı and ca are acceleration factors, both of which are non-negative; rı and r; are random numbers distributed among [0,1]; in each iteration, the fitness value of each particle is determined by the objective function, & P* and the optimal position © id of particles and the optimal position &4 of population are determined by the fitness value. The velocity and position of each particle are adjusted according to the formula that particles update their own velocity and position through individual extremum and population extremum. When the searched position meets the preset minimum fitness value or the iteration times reach the upper limit, the iteration process is stopped.
Another object of the present invention is to provide a big data-based integrated control system for geological disaster prevention and control information service, which implements the big data-based integrated control method for geological disaster prevention and control information service. The integrated control system for geological disaster prevention and control information service based on big data includes:
the rainfall monitoring module is connected with the central control module and used 050 1229 monitoring the rainfall data information in real time through the rainfall sensor, the mud level monitoring module 1s connected with the central control module and used for monitoring the height data information of the mud layer at the bottom of the hillside ditch in real time through the thickness sensor; the vibration monitoring module is connected with the central control module and used for monitoring the ground motion data information in real time through the vibration sensor; the central control module is connected with the rainfall monitoring module, the mud level monitoring module, the vibration monitoring module, the wireless communication module, the disaster identification module, the risk assessment module, the alarm module and the display module, and is used for controlling each module to work normally through the singlechip; the wireless communication module is connected with the central control module, the cloud server and the smartphone, and is used for sending the monitored data to the big data resources centralized with the server for processing through the wireless transmitter, and sending the monitored data to the smartphone for real-time acquisition; the disaster identification module is connected with the central control module and used for identifying geological disaster areas according to satellite images through data processing software; risk assessment module, connected with the central control module, used to assess the risk of geological disasters according to the monitored data through the assessment program; the alarm module is connected with that central control module and used for give an alarm in time according to the monitored abnormal data through the alarm; the display module is connected with the central control module and used for displaying the control system interface and monitored data information of rainfall, mud level and vibration through the display.
Another object of the present invention is to provide an information data processing terminal applying the integrated control method of geological disaster prevention information service based on big data.
The invention has the advantages and positive effects that the disaster identification module combines 3S technologies, namely GPS (Global Positioning System), RS (Remote Sensing) and GIS (Geographic Information System), to locate and analyze the geological disaster area, and finally realize the identification of the scale and harm degree of geological disasters, which 18,25 1229 convenient, fast, low-cost and effective way of disaster identification and judgment. At the same time, through the risk assessment module, remote sensing technology can automatically extract a variety of geological disaster factors, and carry out quantitative statistical analysis on the extracted geological disaster factors, thus accurately and efficiently realizing the assessment function of geological disaster risk, playing a role in preventing geological disasters in advance, and further minimizing the losses caused by geological disasters. The results of geological disaster risk assessment are highly reliable and beneficial to the geological route selection of highway traffic construction.
In the invention, the vibration monitoring module identifies seismic waves during the real-time monitoring of ground motion data information by the vibration sensor, and in order to improve the anti-interference ability of the vibration sensor and the detection velocity and stability, an improved high-order zero-crossing analysis and detection algorithm is adopted.
In the process of real-time monitoring rainfall data information through the rainfall sensor by the rainfall monitoring module in the invention, in order to make the rainfall sensor have better input and output, improve the anti-interference ability and improve the measurement accuracy, the sensor nonlinear dynamic compensation algorithm based on the least square support vector machine is adopted. In the process that the mud level monitoring module monitors the height data information of the mud layer at the bottom of the hillside ditch in real time through the thickness sensor, the thickness sensor is easily influenced by the temperature, which leads to the decrease of the sensitivity coefficient and the increase of the measurement error. In order to avoid the influence of the temperature, the particle swarm optimization neural network algorithm is adopted.
BRIEF DESCRIPTION OF THE FIGURES Fig. 1 is a flowchart of an integrated control method of geological disaster prevention and control information service based on big data provided by an embodiment of the present invention.
Fig. 2 is a structural schematic diagram of the integrated control system of geological disaster prevention and control information service based on big data provided by the embodiment of the present invention; Figure: 1. rainfall monitoring module; 2. mud level monitoring module; 3. vibration monitoring module; 4. central control module; 5. wireless communication module; 6. cloud 501 229 server; 7. smartphone; 8. disaster identification module; 9. risk assessment module; 10. alarm module; 11. display module.
DESCRIPTION OF THE INVENTION In order to further understand the inventive content, characteristics and efficacy of the present invention, the following examples are given and described in detail with the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in Fig. 1, the integrated control method of geological disaster prevention and control information service based on big data provided by the embodiment of the present invention includes the following steps: S101: firstly, collecting rainfall data information, height data information of sludge layer at the bottom of hillside ditch and ground motion data information; S102: analyzing the above data signals, carrying out risk assessment, and when the risk assessment results reach a certain level, identifying that disasters will occur; S103: if it is recognized that a disaster will occur, the information will be sent to the cloud server and smartphone, and the corresponding judgment results will be provided for people, and an alarm will be given by an alarm; S104: finally, the detected data and the corresponding judgment result are displayed on the display screen.
As shown in fig. 2, the integrated control system of geological disaster prevention and control information service based on big data provided by the embodiment of the present invention includes: rainfall monitoring module 1, mud level monitoring module 2, vibration monitoring module 3, central control module 4, wireless communication module 5, cloud server 6, smartphone 7, disaster identification module 8, risk assessment module 9, alarm module 10 and display module 11.
The rainfall monitoring module 1 is connected with the central control module 4 and used for monitoring the rainfall data information in real time through the rainfall sensor; the mud level monitoring module 2 is connected with the central control module 4 and used for monitoring the height data information of the mud layer at the bottom of the hillside ditch in real time through the thickness sensor; LU501229 the vibration monitoring module 3 is connected with the central control module 4 and used for monitoring the ground motion data information in real time through the vibration sensor; the central control module 4 is connected with the rainfall monitoring module 1, the mud level monitoring module 2, the vibration monitoring module 3, the wireless communication module 5, the disaster identification module 8, the risk assessment module 9, the alarm module and the display module 11, and is used for controlling each module to work normally through the singlechip; the wireless communication module 5 is connected with the central control module 4, the cloud server 6 and the smartphone 7, and is used for sending the monitored data to the big data resources centralized with the server 6 for processing through the wireless transmitter, and sending the monitored data to the smartphone 7 for real-time acquisition; the disaster identification module 8 is connected with the central control module 4 and used for identifying geological disaster areas according to satellite images through data processing software: risk assessment module 9, connected with the central control module 4, used to assess the risk of geological disasters according to the monitored data through the assessment program; the alarm module 10 is connected with that central control module 4 and used for give an alarm in time according to the monitored abnormal data through the alarm; the display module 11 is connected with the central control module 4 and used for displaying the control system interface and monitored data information of rainfall, mud level and vibration through the display.
When the rainfall monitoring module 1 monitors the rainfall data information in real time through the rainfall sensor, in order to make the rainfall sensor have better input and output, improve the anti-interference ability and improve the measurement accuracy, the sensor nonlinear dynamic compensation algorithm based on the least square support vector machine 1s adopted, which includes the following steps: step 1, dynamically testing the sensor to measure the input signal u(t) and the output response y(t) of the sensor; at the same time, the ideal (expected) response model 1s established and the signal u(t) is input to obtain the ideal response u' (t); step 2, in the ‘tp ata formula, it is the intermediate model parameter:
u fan pare ain Bin Ba WY LF LU501229 sample data: aids ln edd ou {en av SE een Stead yo Pt Te TY the expected compensation output u'(t), the measured output signal y(t), organize the training sample set {n(t),u(t)} and substitute it into the formula: semen abe HE, get the intermediate model parameter X and get the intermediate model parameter ©; step 3, refer to “ RS BA Or Fesses ; wherein As a oa’ , Be i BEE and Ine Rea wherein © heal 2 according to the intermediate parameter vector © and the corresponding elements, the matrices ¢ and A are constructed, and the parameter vector C and the linear dynamic link parameter vector B of the compensator are obtained by substituting the DO = CATO C= —2 B--4 org formula © A, doo ; step 4, the mathematical analytical expression of compensator model is given through the identified nonlinear compensator parameter vectors A, B and C, and the nonlinear dynamic compensator 1s constructed.
When the mud level monitoring module 2 monitors the height data information of the mud layer at the bottom of the hillside ditch in real time through the thickness sensor, the thickness sensor is easily affected by the temperature, which leads to the decrease of the sensitivity coefficient, thus increasing the measurement error. In order to avoid the influence of the temperature, the particle swarm optimization neural network algorithm is adopted, and the specific process is as follows: firstly, the particle swarm optimization algorithm randomly initializes a particle swarm, and the characteristics of each particle are represented by three indexes of fitness value, position and velocity, and the optimal solution is found through iteration; assume that in a D-dimensional search space, in a D-dimensional search space, the population consisting of n particles is X = (Xi, X 2, ..., Xn), each particle 1 represents a D-dimensional vector, and Xi =[xi1, Xi, ..., xip]!, represents the position of the particle in the
D-dimensional search space; the fitness value corresponding to the position X; of each particle 50 1229 calculated according to the objective function can be expressed in the form of standard deviation, mean square deviation or variance; the velocity of the ith particle is Vi =[Vi1, Vi, … Vip]", and its individual extremum is Pi =[Pi, Pi, ..., Pip]’; the global extremum of the race is Py =[Ps1, Pa, Pep]*"; the formula for particles to update their velocity and position through individual extremum and population extremum 1s as follows: Va = OV tor (Pi 7 Xi) + or, (Pa — Xi) .
x" _ x" + y! id id id ; where © is the inertia weight; d=1, 2,...,D; 1=1,2...,n; k is the velocity of the particle for the contemporary iteration number Vig; cı and ca are acceleration factors, both of which are non-negative; rı and r; are random numbers distributed among [0,1]; in each iteration, the fitness value of each particle is determined by the objective function, & P* and the optimal position #* id of particles and the optimal position &d of population are determined by the fitness value. The velocity and position of each particle are adjusted according to the formula that particles update their own velocity and position through individual extremum and population extremum. When the searched position meets the preset minimum fitness value or the iteration times reach the upper limit, the iteration process is stopped.
The vibration monitoring module 2 identifies seismic waves during the real-time monitoring of ground motion data information by the vibration sensor. In order to improve the anti-interference ability of the vibration sensor and the detection velocity and stability, an improved high-order zero-crossing analysis and detection algorithm is adopted, which specifically includes the following steps: step 1, firstly, after averaging, the signal is divided into small windows which meet the threshold length and the like according to the zero-crossing point by the ordinary zero-crossing analysis method; step 2, calculating the improved zero-crossing number of each small window, judging according to the size relationship between the zero-crossing number of each small window and the threshold set according to the ring mirror noise, and if the zero-crossing number of the window is greater than the set threshold, indicating that there is no target signal in the time,59 1229 period of this window; on the contrary, it indicates that a target appears; step 3, merging seismic wave signals, and processing two target signal windows with signals generated one after another. Because the time interval between two adjacent seismic wave signals is about 0.5 s, if there are only two signals within 1.5 s, there will always be a target within 1.5 s; if there is only one instantaneous signal with a length less than 0.3 s within 1.5 s, it is considered as a false alarm and no target appears; if the signal keeps appearing within 1.5 s or within 0.5 s, it will also be reported that the target appears.
The identification method of the disaster identification module 8 provided by the invention is as follows: (1) according to historical disaster data and meteorological status, preliminarily locating the geological disaster area; (2) according to the preliminarily located disaster location, obtaining the satellite image data of the location; (3) automatically identifying the geological occurrence area in the satellite image by computer, and extracting the automatically identified geological area; (4) according to the automatically identified location of the geological region, the DEM data of the location is obtained; (S) image fusion of satellite images and DEM data, and image enhancement to generate 3D topographic images with elevation information; (6) based on 3D topographic images, manually correct the automatically identified geological areas; (7) according to the corrected mud geological identification area, draw the regional image of geological disasters, and combine with other data to judge the possibility, scale and control scheme of further disasters.
The step (1) provided by the invention preliminarily locates the debris flow disaster area according to historical disaster data and meteorological status, which specifically includes: according to the data, experts preliminarily predict and locate the geological disaster-prone areas through experience, or technicians take GPS equipment to the geological disaster-prone areas to preliminarily locate the disaster areas.
In the step (2) provided by the invention, the acquired satellite image data is a TM image with 7 bands. LU501229 The step (3) provided by the invention, before automatically identifying the debris flow occurrence area in the satellite image by computer, also includes the step of image preprocessing for the satellite image, wherein the image preprocessing includes geometric correction, multi-band digital synthesis and image mosaic.
The assessment method of the risk assessment module 9 provided by the present invention is as follows: 1) based on the high-resolution satellite remote sensing technology, several regional digital terrain models (DEM) are established, meanwhile, high-resolution orthophoto images (DOM) of several regions are generated, remote sensing information data of different regions are recorded and stored, and geological disaster factors of each region are extracted; 2) quantitative statistical analysis of geological disaster factors in step 1); 3) for the quantitative statistical analysis described in step 2), two different data processing methods are adopted, including quantitative processing and qualitative processing, so as to realize the quantitative expression of geological disaster risk.
In step 1) provided by the invention, the methods for extracting geological disaster factors include GIS analysis based on DEM and geological remote sensing interpretation based on image.
The DEM-based GIS analysis steps provided by the invention are as follows: firstly, establish ARCGIS platform and evaluation index system; then, use CreateFishnet on ARCGIS platform to build grid; then, by using the Intersect tool on ARCGIS platform, each map of geological disaster factors is superimposed with the grid, that is, the relationship between geological disaster factors and the grid is established. The above is only a preferred embodiment of the present invention, and it does not limit the present invention in any form. Any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the scope of the technical scheme of the present invention.
Claims (5)
1. An integrated control method of geological disaster prevention and control information service based on big data, characterized in that the integrated control method of geological disaster prevention and control information service based on big data comprises: step 1, collecting rainfall data information, height data information of sludge layer at the bottom of hillside ditch and ground motion data information; step 2, analyze the above data signals and conduct risk assessment; when the risk assessment results reach a certain level, it is identified that disasters will occur; step 3, when it is recognized that a disaster will occur, the information is sent to the cloud server and smartphone to provide people with corresponding judgment results and give an alarm through the alarm; step 4, the detected data and the corresponding judgment result are displayed on the display screen.
2. The integrated control method of geological disaster prevention and control information service based on big data as claimed in claim 1, characterized in that the rainfall data information is monitored in real time by a rainfall sensor, and a sensor nonlinear dynamic compensation algorithm based on least square support vector machine is adopted, which comprises the following steps: step 1, dynamically testing the sensor to measure the input signal u(t) and the output response y(t) of the sensor; at the same time, the ideal (expected) response model is established and the signal u(t) is input to obtain the ideal response u' (t); step 2, in the” as formula, it is the intermediate model parameter: bro Pen Spam nie Se Baier Bud à sample data: picks iu God cu {tn or fe car fem oc ati after dl; the expected compensation output u'(t), the measured output signal y(t), organize the training sample set {n(t),u(t)} and substitute it into the formula: sessed abe vw MY get the intermediate model parameter X and get the intermediate model parameter ©;
step 3, refer to B= or FêmA 8 ; wherein fait sie taf LUS01229 fem bare RAS : and [ba d'a Loh Ee wherein Ciel vey] Ë according to the intermediate parameter vector © and the corresponding elements, the matrices ¢ and A are constructed, and the parameter vector C and the linear dynamic link parameter vector B of the compensator are obtained by substituting the DO = CA" 9>C= 28 B=-J4 ¢7g formula © A, doo ; step 4, the mathematical analytical expression of compensator model is given through the identified nonlinear compensator parameter vectors A, B and C, and the nonlinear dynamic compensator 1s constructed.
3. The integrated control method of geological disaster prevention and control information service based on big data according to claim 1, characterized in that the height data information of the sludge layer at the bottom of the hillside ditch is monitored in real time by a thickness sensor, and the particle swarm optimization neural network algorithm is adopted, and the specific process 1s as follows: firstly, the particle swarm optimization algorithm randomly initializes a particle swarm, and the characteristics of each particle are represented by three indexes of fitness value, position and velocity, and the optimal solution is found through iteration; in a D-dimensional search space, the population consisting of n particles is X = (Xi, Xo, ..., Xn), each particle i represents a D-dimensional vector, and Xi =[xi, Xi, ..., Xin] represents the position of the particle in the D-dimensional search space; the fitness value corresponding to the position X; of each particle calculated according to the objective function can be expressed in the form of standard deviation, mean square deviation or variance; the velocity of the ith particle is Vi =[Vi, Vi, ..., Vin], and its individual extremum is Pi =[Pi, Pi, ..., Pin]; the global extremum of the race is Ps =[Pg1, Ps, ... Pep]; the formula for particles to update their velocity and position through individual extremum and population extremum is as follows: Va = Va + er (Py X) + cpr, (Pa — X a) .
xe = xt + ye LU501229 id id id : where © is the inertia weight; d=1, 2,....D; 1—1,2...,n; k is the velocity of the particle for the contemporary iteration number Vig; cı and ca are acceleration factors, both of which are non-negative; rı and r; are random numbers distributed among [0,1]; in each iteration, the fitness value of each particle is determined by the objective function, & P* and the optimal position #* Zed of particles and the optimal position &d of population are determined by the fitness value; the velocity and position of each particle are adjusted according to the formula that particles update their own velocity and position through individual extremum and population extremum; when the searched position meets the preset minimum fitness value or the iteration times reach the upper limit, the iteration process is stopped.
4. An integrated control system of geological disaster prevention and control information service based on big data for implementing the integrated control method of geological disaster prevention and control information service based on big data according to claim 1, characterized in that the integrated control system of geological disaster prevention and control information service based on big data comprises: the rainfall monitoring module is connected with the central control module and used for monitoring the rainfall data information in real time through the rainfall sensor; the mud level monitoring module is connected with the central control module and used for monitoring the height data information of the mud layer at the bottom of the hillside ditch in real time through the thickness sensor; the vibration monitoring module is connected with the central control module and used for monitoring the ground motion data information in real time through the vibration sensor; the central control module is connected with the rainfall monitoring module, the mud level monitoring module, the vibration monitoring module, the wireless communication module, the disaster identification module, the risk assessment module, the alarm module and the display module, and is used for controlling each module to work normally through the single chip; the wireless communication module is connected with the central control module, the cloud server and the smartphone, and is used for sending the monitored data to the big data resources centralized with the server for processing through the wireless transmitter, and sending the monitored data to the smartphone for real-time acquisition;
the disaster identification module is connected with the central control module and used 0150 1229 identifying geological disaster areas according to satellite images through data processing software: risk assessment module, connected with the central control module, used to assess the risk of geological disasters according to the monitored data through the assessment program; the alarm module is connected with that central control module and used for give an alarm in time according to the monitored abnormal data through the alarm; the display module is connected with the central control module and used for displaying the control system interface and monitored data information of rainfall, mud level and vibration through the display.
5. An information data processing terminal applying the integrated control method of geological disaster prevention information service based on big data according to any one of claims 1 to 3.
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