CN112150769A - Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data - Google Patents
Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data Download PDFInfo
- Publication number
- CN112150769A CN112150769A CN202011026468.XA CN202011026468A CN112150769A CN 112150769 A CN112150769 A CN 112150769A CN 202011026468 A CN202011026468 A CN 202011026468A CN 112150769 A CN112150769 A CN 112150769A
- Authority
- CN
- China
- Prior art keywords
- sub
- area
- rock mass
- pressure
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The invention discloses a geological disaster rock mass collapse intelligent monitoring and early warning system based on big data, which comprises a region division module, a detection point distribution module, a pressure detection module, a pressure parameter processing module, a pressure analysis module, an image acquisition module, an image processing module, a crack area detection module, a crack area analysis module, an analysis server, an early warning reminding module, a display terminal and a storage database, wherein the region division module is used for dividing a region of a geological disaster rock mass collapse area; according to the method, the rock mass area to be detected is divided, the average value of the pressure at the central point of each sub-area in each acquisition time period is detected, whether each sub-area rock mass is in the safety stage or not is analyzed, the image acquisition is carried out on the rock mass surface of each sub-area in the danger stage, the crack area in the rock mass surface image of each sub-area is measured at the same time, the proportion of the crack area in the rock mass of each sub-area is calculated, whether each sub-area rock mass has a collapse risk or not is judged through comparison, and early warning reminding is carried out, so that the safety.
Description
Technical Field
The invention relates to the field of geological disaster monitoring and early warning, in particular to a geological disaster rock mass collapse intelligent monitoring and early warning system based on big data.
Background
The rock mass collapse is a common unfavorable geological disaster in mountainous areas, has the characteristics of strong burst property and large destructive power, and seriously threatens normal production and life of traffic key roads and residents in the mountainous areas; in China, a great amount of casualties and major economic losses are caused by rock mass collapse every year.
At present, there are some not enough in the ordinary existence of traditional monitoring technology that collapses of rock mass, traditional monitoring technology that collapses of rock mass carries out single monitoring to rock mass subregion mostly, can't carry out real-time supervision to whole rock mass region, there is great monitoring limitation, it is inaccurate to cause the monitoring data, lead to the wrong early warning, traditional monitoring method is that people utilize the scale direct measurement rock mass to collapse the back and appear obvious cracked width change, measuring accuracy is relatively poor like this, there is certain danger, rock mass collapses and has the extremely strong characteristics of proruption nature simultaneously, can't realize the early warning effect before the rock mass collapses and destroy, thereby influence road traffic incident, threaten peripheral resident's security of the lives and properties, in order to solve above problem, design an intelligent monitoring and early warning system that collapses of geological disasters rock mass based on big data now.
Disclosure of Invention
The invention aims to provide an intelligent monitoring and early warning system for rock mass collapse in geological disasters based on big data.
The purpose of the invention can be realized by the following technical scheme:
an intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data comprises a region dividing module, a detection point distribution module, a pressure detection module, a pressure parameter processing module, a pressure analysis module, an image acquisition module, an image processing module, a crack area detection module, a crack area analysis module, an analysis server, an early warning reminding module, a display terminal and a storage database;
the analysis server is respectively connected with the pressure analysis module, the image acquisition module, the crack area analysis module, the promotion storage database, the early warning reminding module and the display terminal, the storage database is respectively connected with the region division module, the pressure analysis module and the crack area analysis module, the pressure detection module is respectively connected with the detection point distribution module and the pressure parameter processing module, the pressure parameter processing module is connected with the pressure analysis module, the image processing module is respectively connected with the image acquisition module and the crack area detection module, and the crack area detection module is connected with the crack area analysis module;
the region dividing module is used for dividing a rock mass region to be detected, dividing the rock mass region into a plurality of subregions with the same area according to a gridding equal division mode, numbering the plurality of divided subregions sequentially from left to right and from bottom to top, wherein the numbering is 1,2, a.
The detection point distribution module is used for distributing detection points to the plurality of divided sub-areas, the detection points are distributed at the central points of the sub-areas in an evenly distributed mode, and the detection points correspond to the sub-areas one by one to form a detection point number set A at the central point of each sub-arean(a1,a2,...,ai,...,an)，aiThe number of the detection point at the central point of the ith sub-area is represented, and a set of the numbers of the detection points at the central points of the sub-areas is sent to the pressure detection module;
the pressure detection module comprises a plurality of pressure sensors, the pressure sensors are used for receiving a detection point number set of each sub-area central point sent by the detection point distribution module, the pressure sensors are installed in the detection points of each sub-area central point, the pressure sensors are in one-to-one correspondence with the detection points of each sub-area central point, the pressure sensors are used for detecting the pressure of each sub-area central point in real time, and the detected pressure of each sub-area central point is sent to the pressure parameter processing module;
the pressure parameter processing module is used for receiving the pressure at the central point of each sub-area sent by the pressure detection module and receiving the pressure at the central point of each sub-areaThe pressure is divided according to the acquisition time period, and the average value of the pressure at the central point of each sub-area in each acquisition time period is counted to form an average value set Ft (f) of the pressure at the central point of each sub-area in each acquisition time period1t,f2t,...,fit,...,fnt)，fit is the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period, and t is t1,t2,...,txSending the average value set of the pressure at the central point of each sub-area in each acquisition time period to a pressure analysis module;
the pressure analysis module is used for receiving the average value set of the pressure at the central point of each sub-area in each acquisition time period sent by the pressure parameter processing module, extracting the safe pressure value before rock collapse stored in the storage database, comparing the received average value of the pressure at the central point of each sub-area in each acquisition time period with the stored safe pressure value before rock collapse, and obtaining a comparison difference value set delta Ft (delta f) of the average value of the pressure at the central point of each sub-area in each acquisition time period1t,Δf2t,...,Δfit,...,Δfnt)，Δfit is represented as the comparison difference value of the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period and the safe pressure value before rock mass collapse, and t is t1,t2,...,txSending the comparison difference set of the pressure average values of the central points of the sub-areas in each acquisition time period to an analysis server;
the analysis server is used for receiving a comparison difference set of pressure average values of the central points of the sub-regions in each acquisition time period sent by the pressure analysis module and extracting the tthxComparison difference value set delta Ft of pressure average values at central points of all sub-areas in each acquisition time periodx(Δf1tx,Δf2tx,...,Δfitx,...,Δfntx) If it is txThe contrast difference value of the pressure average value at the central point of a sub-area in each acquisition time period is less than or equal to zero, which indicates that the rock mass of the sub-area is in a safe stage, if the tthxPressure level at the center point of a sub-region in each acquisition time periodThe comparison difference value of the average value is larger than zero, the rock mass of the sub-region is in a dangerous stage, the serial numbers of the sub-regions of the rock mass in the dangerous stage are counted, the counted comparison difference values of the pressure average values at the central points of the sub-regions are sequentially arranged from large to small, and the arranged sub-regions of the rock mass in the dangerous stage are sent to an image acquisition module;
the image acquisition module comprises a high-definition camera, wherein the high-definition camera is installed on the unmanned aerial vehicle and used for receiving all the subareas of the arranged rock mass in the dangerous stage sent by the analysis server, sequentially carrying out image acquisition on the rock mass surfaces of all the subareas through the high-definition camera of the unmanned aerial vehicle, and sending the acquired rock mass surface images of all the subareas after arrangement to the image processing module;
the image processing module is used for receiving the rock mass surface images of the arranged sub-regions sent by the image acquisition module, performing image segmentation on the received rock mass surface images of the arranged sub-regions, selecting a minimum region of the rock mass surface wrapping the sub-regions, removing the images outside the minimum region, performing gray processing and image enhancement processing on the retained minimum region images to obtain enhanced rock mass surface images of the arranged sub-regions, and sending the enhanced rock mass surface images of the arranged sub-regions to the crack area detection module;
the crack area detection module is used for receiving the enhanced rock mass surface images of the arranged subregions sent by the image processing module, measuring cracks in the enhanced rock mass surface images of the arranged subregions, measuring the sizes of the cracks in the rock mass surface images of the arranged subregions, analyzing and acquiring the crack areas in the rock mass surface images of the arranged subregions, counting the crack areas in the rock mass surface images of the arranged subregions, and forming a crack area set S (S) in the rock mass surface images of the arranged subregions1,s2,...,sj,...,sm),m≤n，sjExpressed as the crack area in the j-th collected rock mass surface image of the sub-region after arrangement, and the crack area in the rock mass surface image of each sub-region after arrangement is aggregated and sent to crack area analysisA module;
the crack area analysis module is used for receiving a crack area set in the rock mass surface images of the arranged sub-regions sent by the crack area detection module, extracting the total area of the rock mass region to be detected stored in the storage database and the ratio of the area of the image shot by the high-definition camera to the real object area, calculating the proportion of the crack area in the rock mass of the arranged sub-regions, and forming a proportion set k (k) of the crack area in the rock mass of the arranged sub-regions1,k2,...,kj,...,km)，kjExpressed as the ratio of the crack area in the j-th collected sub-region rock mass after arrangement to the corresponding sub-region area, and sending the percentage set of the crack area in each sub-region rock mass after arrangement to an analysis server;
the analysis server is used for receiving the arranged percentage set of the crack areas in the rock masses of each sub-region sent by the crack area analysis module, extracting the safety percentage of the crack areas in the rock mass regions stored in the storage database, comparing the percentage of the crack areas in the rock masses of each sub-region with the stored safety percentage of the crack areas in the rock mass regions, if the percentage of the crack areas in the rock masses of a certain sub-region after arrangement is smaller than or equal to the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has no collapse risk, if the percentage of the crack areas in the rock mass of a certain sub-region after arrangement is larger than the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has the collapse risk, counting the numbers of the sub-regions with the collapse risk of the rock mass, and sending the numbers of the sub-regions; meanwhile, the analysis server calculates the estimated reaction coefficient of rock mass collapse of each subarea and sends the calculated estimated reaction coefficient of rock mass collapse of each subarea to the display terminal;
the storage database is used for receiving the serial numbers of the sub-regions sent by the region dividing module, simultaneously storing the safety pressure value f' before rock mass collapse and storing the total area s of the rock mass region to be detectedGeneral assemblyThe ratio lambda of the area of the image shot by the high-definition camera to the real object area is stored, and the safe occupation ratio k' of the crack area in the rock body area is stored;
the early warning reminding module is used for receiving the numbers of the sub-regions of the rock mass with the collapse risk sent by the analysis server, carrying out early warning reminding, and carrying out emergency precautionary measures by related personnel according to the number of each sub-region to be reminded;
the display terminal is used for receiving the estimated reaction coefficient of rock mass collapse of each sub-area sent by the analysis server and displaying the estimated reaction coefficient;
further, the calculation formula of the proportion of the crack area in the rock mass of each subarea after arrangement is shown as
kjExpressed as the ratio of the crack area in the rock mass of the j-th collected subregion after arrangement to the area of the corresponding subregion, sjExpressed as the crack area, s, in the arranged j-th collected subregion rock mass surface imageGeneral assemblyThe total area of the rock mass area to be detected is represented, and the lambda is represented as the ratio of the area of the image shot by the high-definition camera to the real object area;
further, the calculation formula of the estimated reaction coefficient of rock mass collapse of each sub-area is
ξiExpressed as the estimated reaction coefficient, f, of rock mass collapse in the ith sub-regionitxDenoted as txAverage value of pressure at the center point of the ith sub-region in each acquisition time period, fitx-1Denoted as tx-1The average value of the pressure at the central point of the ith sub-area in each acquisition time period, f' represents the safe pressure value before rock mass collapse, and delta fitxDenoted as txThe average value of the pressure at the central point of the ith sub-area in each acquisition time period is compared with the safety pressure value before rock mass collapse, e is a natural number and is equal to 2.718, kjExpressed as the ratio of the area of the crack in the rock mass of the ith sub-region to the area of the corresponding sub-region, and k' is expressed as the safe ratio of the area of the crack in the rock mass region.
Has the advantages that:
(1) the invention provides an intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data, which divides a rock mass area to be detected through a region dividing module, detects the average value of the pressure at the central point of each sub-region in each acquisition time period, thereby avoiding inaccurate monitoring data caused by the problem of detection leakage, obtains the comparison difference value of the average value of the pressure at the central point of each sub-region in each acquisition time period by comparison, provides reliable reference basis for calculating the estimated reaction coefficient of rock mass collapse of each sub-region in the later period, analyzes whether the rock mass of each sub-region is in a safe stage, acquires images of the rock mass surface of each sub-region in a dangerous stage, avoids the occurrence of error early warning phenomenon, thereby wasting labor cost and time cost, simultaneously measures the crack area in the rock mass surface image of each sub-region, calculates the proportion of the crack area in the rock mass of each sub-, therefore, the accuracy of data is improved, life danger caused by personnel monitoring is avoided, whether the rock mass of each sub-region has collapse risk or not is judged through comparison, and early warning is carried out on each sub-region with the collapse risk in the rock mass, so that the incidence rate of road traffic safety accidents can be reduced, and the life and property safety of surrounding residents is guaranteed.
(2) According to the method, the pre-estimated reaction coefficient of rock mass collapse of each sub-region is calculated through the analysis server and is displayed through the display terminal, the condition of the rock mass of each sub-region before collapse can be visually displayed, the rock mass collapse damage of each sub-region can be pre-estimated, the method has the characteristic of high accuracy, relevant personnel can conveniently perform emergency precautionary measures, and unnecessary casualty accidents caused by confusion are prevented.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of the present invention;
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, an intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data comprises a region division module, a detection point distribution module, a pressure detection module, a pressure parameter processing module, a pressure analysis module, an image acquisition module, an image processing module, a crack area detection module, a crack area analysis module, an analysis server, an early warning reminding module, a display terminal and a storage database;
the analysis server is respectively connected with the pressure analysis module, the image acquisition module, the crack area analysis module, the promotion storage database, the early warning reminding module and the display terminal, the storage database is respectively connected with the region division module, the pressure analysis module and the crack area analysis module, the pressure detection module is respectively connected with the detection point distribution module and the pressure parameter processing module, the pressure parameter processing module is connected with the pressure analysis module, the image processing module is respectively connected with the image acquisition module and the crack area detection module, and the crack area detection module is connected with the crack area analysis module;
the region dividing module is used for dividing a rock mass region to be detected, dividing the rock mass region into a plurality of subregions with the same area according to a gridding equal division mode, numbering the plurality of divided subregions sequentially from left to right and from bottom to top, wherein the numbering is 1,2, a.
The detection point distribution module is used for distributing detection points to the plurality of divided sub-areas, the detection points are distributed at the central points of the sub-areas in an evenly distributed mode, and the detection points correspond to the sub-areas one to form the detection point distribution moduleDetection point number set A at central point of each sub-arean(a1,a2,...,ai,...,an)，aiThe number of the detection point at the central point of the ith sub-area is represented, and a set of the numbers of the detection points at the central points of the sub-areas is sent to the pressure detection module;
the pressure detection module comprises a plurality of pressure sensors, the pressure sensors are used for receiving a detection point number set of each sub-area central point sent by the detection point distribution module, the pressure sensors are installed in the detection points of each sub-area central point, the pressure sensors are in one-to-one correspondence with the detection points of each sub-area central point, the pressure sensors are used for detecting the pressure of each sub-area central point in real time, and the detected pressure of each sub-area central point is sent to the pressure parameter processing module;
the pressure parameter processing module is used for receiving the pressure of the central point of each subregion sent by the pressure detection module, dividing the received pressure of the central point of each subregion according to the acquisition time period, and counting the average value of the pressure of the central point of each subregion in each acquisition time period, thereby avoiding the inaccurate monitoring data caused by the detection leakage problem and forming the average value set Ft (f) of the pressure of the central point of each subregion in each acquisition time period1t,f2t,...,fit,...,fnt)，fit is the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period, and t is t1,t2,...,txSending the average value set of the pressure at the central point of each sub-area in each acquisition time period to a pressure analysis module;
the pressure analysis module is used for receiving the average value set of the pressure at the central point of each sub-area in each acquisition time period sent by the pressure parameter processing module, extracting the safe pressure value before rock collapse stored in the storage database, comparing the received average value of the pressure at the central point of each sub-area in each acquisition time period with the stored safe pressure value before rock collapse, and obtaining a comparison difference value set delta Ft (delta f) of the average value of the pressure at the central point of each sub-area in each acquisition time period1t,Δf2t,...,Δfit,...,Δfnt)，Δfit is represented as the comparison difference value of the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period and the safe pressure value before rock mass collapse, and t is t1,t2,...,txProviding reliable reference basis for calculating the estimated reaction coefficient of rock mass collapse of each subregion in the later period, and sending a comparison difference set of the pressure average values of the central points of each subregion in each acquisition time period to an analysis server;
the analysis server is used for receiving a comparison difference set of pressure average values of the central points of the sub-regions in each acquisition time period sent by the pressure analysis module and extracting the tthxComparison difference value set delta Ft of pressure average values at central points of all sub-areas in each acquisition time periodx(Δf1tx,Δf2tx,...,Δfitx,...,Δfntx) If it is txThe contrast difference value of the pressure average value at the central point of a sub-area in each acquisition time period is less than or equal to zero, which indicates that the rock mass of the sub-area is in a safe stage, if the tthxThe comparison difference of the pressure average values at the central point of a certain sub-area in each acquisition time period is larger than zero, the rock mass of the sub-area is shown to be in a dangerous stage, the serial numbers of the sub-areas of the rock mass in the dangerous stage are counted, the counted comparison difference of the pressure average values at the central point of each sub-area is sequentially arranged from large to small, and the arranged sub-areas of the rock mass in the dangerous stage are sent to the image acquisition module;
the image acquisition module comprises a high-definition camera, wherein the high-definition camera is installed on the unmanned aerial vehicle and used for receiving all the subareas of the arranged rock mass in the dangerous stage sent by the analysis server, and the high-definition camera of the unmanned aerial vehicle is used for sequentially acquiring the images of the rock mass surfaces of all the subareas after arrangement, so that the phenomenon of error early warning is avoided, the labor cost and the time cost are wasted, and the acquired rock mass surface images of all the subareas after arrangement are sent to the image processing module;
the image processing module is used for receiving the rock mass surface images of the arranged sub-regions sent by the image acquisition module, performing image segmentation on the received rock mass surface images of the arranged sub-regions, selecting a minimum region of the rock mass surface wrapping the sub-regions, removing the images outside the minimum region, performing gray processing and image enhancement processing on the retained minimum region images to obtain enhanced rock mass surface images of the arranged sub-regions, and sending the enhanced rock mass surface images of the arranged sub-regions to the crack area detection module;
the crack area detection module is used for receiving the enhanced rock mass surface images of the arranged subregions sent by the image processing module, measuring cracks in the enhanced rock mass surface images of the arranged subregions, measuring the sizes of the cracks in the rock mass surface images of the arranged subregions, analyzing and acquiring the crack areas in the rock mass surface images of the arranged subregions, counting the crack areas in the rock mass surface images of the arranged subregions, and forming a crack area set S (S) in the rock mass surface images of the arranged subregions1,s2,...,sj,...,sm),m≤n，sjThe area of the cracks in the rock mass surface images of the j-th collected sub-region after arrangement is represented, and the crack area in the rock mass surface images of each sub-region after arrangement is collected and sent to a crack area analysis module;
the crack area analysis module is used for receiving a crack area set in the rock mass surface images of the arranged sub-regions sent by the crack area detection module, extracting the total area of the rock mass region to be detected stored in the storage database and the ratio of the area of the image shot by the high-definition camera to the real object area, and calculating the proportion of the crack area in the rock mass of the arranged sub-regions, so that the accuracy of data is improved, the life risk caused by personnel monitoring is avoided, and the proportion calculation formula of the crack area in the rock mass of the arranged sub-regions is
kjExpressed as the ratio of the crack area in the rock mass of the j-th collected subregion after arrangement to the area of the corresponding subregion, sjExpressed as the crack area, s, in the arranged j-th collected subregion rock mass surface imageGeneral assemblyIs indicated as being to be examinedThe measured total area of the rock mass region, lambda, is expressed as the ratio of the area of the image shot by the high-definition camera to the real object area, and forms a proportion set k (k) of the crack areas in the rock mass of each subarea after arrangement1,k2,...,kj,...,km)，kjAnd expressing the ratio of the crack area in the jth collected sub-region rock mass after arrangement to the corresponding sub-region area, and sending the ratio set of the crack areas in the rock masses of all the sub-regions after arrangement to an analysis server.
The analysis server is used for receiving the arranged percentage set of the crack areas in the rock masses of each sub-region sent by the crack area analysis module, extracting the safety percentage of the crack areas in the rock mass regions stored in the storage database, comparing the percentage of the crack areas in the rock masses of each sub-region with the stored safety percentage of the crack areas in the rock mass regions, if the percentage of the crack areas in the rock masses of a certain sub-region after arrangement is smaller than or equal to the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has no collapse risk, if the percentage of the crack areas in the rock mass of a certain sub-region after arrangement is larger than the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has the collapse risk, counting the numbers of the sub-regions with the collapse risk of the rock mass, and sending the numbers of the sub-regions;
meanwhile, the analysis server calculates the estimated reaction coefficient of rock mass collapse of each subarea, and the calculation formula of the estimated reaction coefficient of rock mass collapse of each subarea is
ξiExpressed as the estimated reaction coefficient, f, of rock mass collapse in the ith sub-regionitxDenoted as txAverage value of pressure at the center point of the ith sub-region in each acquisition time period, fitx-1Denoted as tx-1The average value of the pressure at the central point of the ith sub-area in each acquisition time period, f' represents the safe pressure value before rock mass collapse, and delta fitxDenoted as txAverage value of pressure at central point of ith sub-area in each acquisition time period and rockThe comparison difference value of the safety pressure value before the body collapse, e is expressed as a natural number and is equal to 2.718, kjAnd the calculated estimated reaction coefficient of rock mass collapse of each sub-region is sent to a display terminal.
The storage database is used for receiving the serial numbers of the sub-regions sent by the region dividing module, simultaneously storing the safety pressure value f' before rock mass collapse and storing the total area s of the rock mass region to be detectedGeneral assemblyAnd the ratio lambda of the area of the image shot by the high-definition camera to the real object area, and the safe occupation ratio k' of the crack area in the rock mass region are stored.
The early warning reminding module is used for receiving the number of each sub-region of the rock mass with the collapse risk sent by the analysis server, carrying out early warning reminding, and carrying out emergency precautionary measures by related personnel according to the number of each sub-region to be reminded, so that the incidence rate of road traffic safety accidents can be reduced, and the life and property safety of surrounding residents can be guaranteed.
The display terminal is used for receiving the pre-estimated reaction coefficients of the rock mass collapse of each sub-region sent by the analysis server, displaying the pre-estimated reaction coefficients, visually displaying the conditions of the rock mass collapse of each sub-region, pre-estimating the rock mass collapse damage of each sub-region, having the characteristic of high accuracy, facilitating the emergency precaution of related personnel, and preventing the occurrence of unnecessary casualty accidents due to disorder.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (3)
1. The utility model provides a geological disaster rock mass intelligent monitoring early warning system that collapses based on big data which characterized in that: the system comprises a region dividing module, a detection point distribution module, a pressure detection module, a pressure parameter processing module, a pressure analysis module, an image acquisition module, an image processing module, a crack area detection module, a crack area analysis module, an analysis server, an early warning reminding module, a display terminal and a storage database;
the analysis server is respectively connected with the pressure analysis module, the image acquisition module, the crack area analysis module, the promotion storage database, the early warning reminding module and the display terminal, the storage database is respectively connected with the region division module, the pressure analysis module and the crack area analysis module, the pressure detection module is respectively connected with the detection point distribution module and the pressure parameter processing module, the pressure parameter processing module is connected with the pressure analysis module, the image processing module is respectively connected with the image acquisition module and the crack area detection module, and the crack area detection module is connected with the crack area analysis module;
the region dividing module is used for dividing a rock mass region to be detected, dividing the rock mass region into a plurality of subregions with the same area according to a gridding equal division mode, numbering the plurality of divided subregions sequentially from left to right and from bottom to top, wherein the numbering is 1,2, a.
The detection point distribution module is used for distributing detection points to the plurality of divided sub-areas, the detection points are distributed at the central points of the sub-areas in an evenly distributed mode, and the detection points correspond to the sub-areas one by one to form a detection point number set A at the central point of each sub-arean(a1,a2,...,ai,...,an)，aiThe number of the detection point at the central point of the ith sub-area is represented, and a set of the numbers of the detection points at the central points of the sub-areas is sent to the pressure detection module;
the pressure detection module comprises a plurality of pressure sensors, the pressure sensors are used for receiving a detection point number set of each sub-area central point sent by the detection point distribution module, the pressure sensors are installed in the detection points of each sub-area central point, the pressure sensors are in one-to-one correspondence with the detection points of each sub-area central point, the pressure sensors are used for detecting the pressure of each sub-area central point in real time, and the detected pressure of each sub-area central point is sent to the pressure parameter processing module;
the pressure parameter processing module is used for receiving the pressure at the central point of each sub-region sent by the pressure detection module, dividing the received pressure at the central point of each sub-region according to the acquisition time period, and counting the average value of the pressure at the central point of each sub-region in each acquisition time period to form an average value set Ft (f) of the pressure at the central point of each sub-region in each acquisition time period1t,f2t,...,fit,...,fnt)，fit is the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period, and t is t1,t2,...,txSending the average value set of the pressure at the central point of each sub-area in each acquisition time period to a pressure analysis module;
the pressure analysis module is used for receiving the average value set of the pressure at the central point of each sub-area in each acquisition time period sent by the pressure parameter processing module, extracting the safe pressure value before rock collapse stored in the storage database, comparing the received average value of the pressure at the central point of each sub-area in each acquisition time period with the stored safe pressure value before rock collapse, and obtaining a comparison difference value set delta Ft (delta f) of the average value of the pressure at the central point of each sub-area in each acquisition time period1t,Δf2t,...,Δfit,...,Δfnt)，Δfit is represented as the comparison difference value of the average value of the pressure at the central point of the ith sub-area in the tth acquisition time period and the safe pressure value before rock mass collapse, and t is t1,t2,...,txSending the comparison difference set of the pressure average values of the central points of the sub-areas in each acquisition time period to an analysis server;
the analysis server is used for receiving a comparison difference set of pressure average values of the central points of the sub-regions in each acquisition time period sent by the pressure analysis module and extracting the tthxComparison difference value set delta Ft of pressure average values at central points of all sub-areas in each acquisition time periodx(Δf1tx,Δf2tx,...,Δfitx,...,Δfntx) If it is txThe contrast difference value of the pressure average value at the central point of a sub-area in each acquisition time period is less than or equal to zero, which indicates that the rock mass of the sub-area is in a safe stage, if the tthxThe comparison difference of the pressure average values at the central point of a certain sub-area in each acquisition time period is larger than zero, the rock mass of the sub-area is shown to be in a dangerous stage, the serial numbers of the sub-areas of the rock mass in the dangerous stage are counted, the counted comparison difference of the pressure average values at the central point of each sub-area is sequentially arranged from large to small, and the arranged sub-areas of the rock mass in the dangerous stage are sent to the image acquisition module;
the image acquisition module comprises a high-definition camera, wherein the high-definition camera is installed on the unmanned aerial vehicle and used for receiving all the subareas of the arranged rock mass in the dangerous stage sent by the analysis server, sequentially carrying out image acquisition on the rock mass surfaces of all the subareas through the high-definition camera of the unmanned aerial vehicle, and sending the acquired rock mass surface images of all the subareas after arrangement to the image processing module;
the image processing module is used for receiving the rock mass surface images of the arranged sub-regions sent by the image acquisition module, performing image segmentation on the received rock mass surface images of the arranged sub-regions, selecting a minimum region of the rock mass surface wrapping the sub-regions, removing the images outside the minimum region, performing gray processing and image enhancement processing on the retained minimum region images to obtain enhanced rock mass surface images of the arranged sub-regions, and sending the enhanced rock mass surface images of the arranged sub-regions to the crack area detection module;
the crack area detection module is used for receiving the enhanced rock mass surface images of the arranged subregions sent by the image processing module, measuring cracks in the enhanced rock mass surface images of the arranged subregions, measuring the sizes of the cracks in the rock mass surface images of the arranged subregions, analyzing and acquiring the crack areas in the rock mass surface images of the arranged subregions, counting the crack areas in the rock mass surface images of the arranged subregions, and forming a crack area set in the rock mass surface images of the arranged subregionsAnd then S (S)1,s2,...,sj,...,sm),m≤n，sjThe area of the cracks in the rock mass surface images of the j-th collected sub-region after arrangement is represented, and the crack area in the rock mass surface images of each sub-region after arrangement is collected and sent to a crack area analysis module;
the crack area analysis module is used for receiving a crack area set in the rock mass surface images of the arranged sub-regions sent by the crack area detection module, extracting the total area of the rock mass region to be detected stored in the storage database and the ratio of the area of the image shot by the high-definition camera to the real object area, calculating the proportion of the crack area in the rock mass of the arranged sub-regions, and forming a proportion set k (k) of the crack area in the rock mass of the arranged sub-regions1,k2,...,kj,...,km)，kjExpressed as the ratio of the crack area in the j-th collected sub-region rock mass after arrangement to the corresponding sub-region area, and sending the percentage set of the crack area in each sub-region rock mass after arrangement to an analysis server;
the analysis server is used for receiving the arranged percentage set of the crack areas in the rock masses of each sub-region sent by the crack area analysis module, extracting the safety percentage of the crack areas in the rock mass regions stored in the storage database, comparing the percentage of the crack areas in the rock masses of each sub-region with the stored safety percentage of the crack areas in the rock mass regions, if the percentage of the crack areas in the rock masses of a certain sub-region after arrangement is smaller than or equal to the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has no collapse risk, if the percentage of the crack areas in the rock mass of a certain sub-region after arrangement is larger than the safety percentage of the crack areas in the rock mass regions, indicating that the rock mass of the sub-region has the collapse risk, counting the numbers of the sub-regions with the collapse risk of the rock mass, and sending the numbers of the sub-regions; meanwhile, the analysis server calculates the estimated reaction coefficient of rock mass collapse of each subarea and sends the calculated estimated reaction coefficient of rock mass collapse of each subarea to the display terminal;
the storage database is used for receiving the numbers of the plurality of sub-areas sent by the area dividing moduleSimultaneously storing the safety pressure value f' before rock mass collapse and storing the total area s of the rock mass area to be detectedGeneral assemblyThe ratio lambda of the area of the image shot by the high-definition camera to the real object area is stored, and the safe occupation ratio k' of the crack area in the rock body area is stored;
the early warning reminding module is used for receiving the numbers of the sub-regions of the rock mass with the collapse risk sent by the analysis server, carrying out early warning reminding, and carrying out emergency precautionary measures by related personnel according to the number of each sub-region to be reminded;
and the display terminal is used for receiving and displaying the estimated reaction coefficients of rock mass collapse of each sub-area sent by the analysis server.
2. The intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data as claimed in claim 1, is characterized in that: the proportion calculation formula of the crack area in the rock mass of each subarea after arrangement is
kjExpressed as the ratio of the crack area in the rock mass of the j-th collected subregion after arrangement to the area of the corresponding subregion, sjExpressed as the crack area, s, in the arranged j-th collected subregion rock mass surface imageGeneral assemblyThe total area of the rock mass area to be detected is represented, and the lambda is represented as the ratio of the area of the image shot by the high-definition camera to the real object area.
3. The intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data as claimed in claim 1, is characterized in that: the calculation formula of the estimated reaction coefficient of rock mass collapse of each subregion is
ξiExpressed as the estimated reaction coefficient, f, of rock mass collapse in the ith sub-regionitxDenoted as txAverage value of pressure at the center point of the ith sub-region in each acquisition time period, fitx-1Denoted as tx-1The average value of the pressure at the central point of the ith sub-area in each acquisition time period, f' represents the safe pressure value before rock mass collapse, and delta fitxDenoted as txThe average value of the pressure at the central point of the ith sub-area in each acquisition time period is compared with the safety pressure value before rock mass collapse, e is a natural number and is equal to 2.718, kjExpressed as the ratio of the area of the crack in the rock mass of the ith sub-region to the area of the corresponding sub-region, and k' is expressed as the safe ratio of the area of the crack in the rock mass region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011026468.XA CN112150769A (en) | 2020-09-25 | 2020-09-25 | Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011026468.XA CN112150769A (en) | 2020-09-25 | 2020-09-25 | Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112150769A true CN112150769A (en) | 2020-12-29 |
Family
ID=73897471
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011026468.XA Withdrawn CN112150769A (en) | 2020-09-25 | 2020-09-25 | Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112150769A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112927480A (en) * | 2021-01-29 | 2021-06-08 | 盐城墨韵电子科技有限公司 | Geological disaster monitoring method and early warning management platform based on Internet of things and big data collaborative analysis |
| CN112964735A (en) * | 2021-02-02 | 2021-06-15 | 南京柏王智能装备科技有限公司 | Rail transit safety intelligent monitoring method based on big data analysis and cloud monitoring platform |
| CN113358850A (en) * | 2021-05-31 | 2021-09-07 | 武汉财源通网络科技有限公司 | Geological exploration survey safety monitoring and early warning system based on unmanned aerial vehicle data acquisition |
| CN113720851A (en) * | 2021-08-02 | 2021-11-30 | 重庆市地质灾害防治中心 | Dangerous rock body acousto-optic combined intelligent monitoring and early warning method |
| CN114596013A (en) * | 2022-05-10 | 2022-06-07 | 山东志诚地理信息技术有限公司 | Geotechnical engineering investigation safety monitoring system based on cloud computing |
| CN115330000A (en) * | 2022-08-31 | 2022-11-11 | 武汉旻一数字科技有限公司 | Intelligent monitoring and management system for operation of industrial automation control instrument |
| CN117852887A (en) * | 2024-01-11 | 2024-04-09 | 铁正检测科技有限公司 | Tunnel geological monitoring management system based on big data |
-
2020
- 2020-09-25 CN CN202011026468.XA patent/CN112150769A/en not_active Withdrawn
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112927480A (en) * | 2021-01-29 | 2021-06-08 | 盐城墨韵电子科技有限公司 | Geological disaster monitoring method and early warning management platform based on Internet of things and big data collaborative analysis |
| CN112927480B (en) * | 2021-01-29 | 2022-11-11 | 中交华南勘察测绘科技有限公司 | Geological disaster monitoring method and early warning management platform based on Internet of things and big data collaborative analysis |
| CN112964735A (en) * | 2021-02-02 | 2021-06-15 | 南京柏王智能装备科技有限公司 | Rail transit safety intelligent monitoring method based on big data analysis and cloud monitoring platform |
| CN113358850A (en) * | 2021-05-31 | 2021-09-07 | 武汉财源通网络科技有限公司 | Geological exploration survey safety monitoring and early warning system based on unmanned aerial vehicle data acquisition |
| CN113720851A (en) * | 2021-08-02 | 2021-11-30 | 重庆市地质灾害防治中心 | Dangerous rock body acousto-optic combined intelligent monitoring and early warning method |
| CN114596013A (en) * | 2022-05-10 | 2022-06-07 | 山东志诚地理信息技术有限公司 | Geotechnical engineering investigation safety monitoring system based on cloud computing |
| CN115330000A (en) * | 2022-08-31 | 2022-11-11 | 武汉旻一数字科技有限公司 | Intelligent monitoring and management system for operation of industrial automation control instrument |
| CN117852887A (en) * | 2024-01-11 | 2024-04-09 | 铁正检测科技有限公司 | Tunnel geological monitoring management system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112150769A (en) | Intelligent monitoring and early warning system for geological disaster rock mass collapse based on big data | |
| CN111486902B (en) | Large-span bridge safety performance real-time monitoring system based on big data | |
| CN110427533B (en) | Pollution propagation mode visual analysis method and system based on time sequence particle tracking | |
| CN112432665A (en) | Hydraulic engineering dam body safety real-time monitoring and early warning management system based on big data analysis | |
| CN112348237B (en) | Abnormal trend detection method for dynamic drilling data | |
| CN113963514A (en) | Integrated monitoring and early warning system for oil gasification pipeline | |
| CN111578150B (en) | Online real-time monitoring of oil gas delivery pipe network safety and early warning management system | |
| CN111796547A (en) | Road and bridge safety intelligent monitoring and early warning management system based on big data | |
| CN111505010A (en) | Bridge safety detection system based on cloud platform | |
| CN112053093A (en) | Geological disaster landslide real-time monitoring management system based on big data | |
| CN112949900B (en) | Reservoir dam safety information intelligent perception fusion early warning method and terminal equipment | |
| CN112380599A (en) | Bridge intelligent online monitoring and early warning system based on big data analysis | |
| CN112859710A (en) | Mine geological safety online real-time monitoring and early warning cloud platform based on artificial intelligence and Internet of things | |
| CN116342598B (en) | Steel strand quality detection method based on machine vision | |
| CN115410342B (en) | Landslide hazard intelligent early warning method based on real-time monitoring of crack meter | |
| CN115376283B (en) | Monitoring and early warning method and system based on multivariate data fusion | |
| CN112414734A (en) | Large-span bridge safety real-time monitoring system based on big data analysis | |
| CN114298549A (en) | Water conservancy safety monitoring system and method based on big data | |
| CN112883847A (en) | Intelligent supervision method for municipal road engineering construction project based on big data and image analysis technology | |
| CN114495428A (en) | Early warning method and system for landslide disaster | |
| CN108562933B (en) | A kind of earthquake magnitude method for quick estimating merging multi-class data source | |
| CN111784785B (en) | Bridge dynamic displacement identification method | |
| CN116990847B (en) | Beidou GNSS receiver resolving method and system based on edge calculation | |
| CN112949735A (en) | Liquid hazardous chemical substance volatile concentration abnormity discovery method based on outlier data mining | |
| CN114001887B (en) | Bridge damage assessment method based on deflection monitoring |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201229 |