CN102013150B - System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation - Google Patents

System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation Download PDF

Info

Publication number
CN102013150B
CN102013150B CN2010102979456A CN201010297945A CN102013150B CN 102013150 B CN102013150 B CN 102013150B CN 2010102979456 A CN2010102979456 A CN 2010102979456A CN 201010297945 A CN201010297945 A CN 201010297945A CN 102013150 B CN102013150 B CN 102013150B
Authority
CN
China
Prior art keywords
soil moisture
rainfall
moisture content
slope
coil
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.)
Active
Application number
CN2010102979456A
Other languages
Chinese (zh)
Other versions
CN102013150A (en
Inventor
汤一平
俞立
田贤园
宗明理
何熊熊
孙福顶
孟炎
叶良波
吴立娟
陈才国
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN2010102979456A priority Critical patent/CN102013150B/en
Publication of CN102013150A publication Critical patent/CN102013150A/en
Application granted granted Critical
Publication of CN102013150B publication Critical patent/CN102013150B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a system for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation. The system comprises an automatic rainfall gauge, a soil moisture sensor, an omnibearing tilt sensor, an omnibearing vision sensor, an embedded system and a monitoring center computer, wherein the automatic rainfall gauge is used for monitoring rainfall and rainfall intensity of a geologic hazard point; the soil moisture sensor is used for monitoring moisture in soil at the geologic hazard point; the omnibearing tilt sensor is used for detecting earth surface deformation and internal deformation of the geologic hazard point; the omnibearing vision sensor is used for evaluating geologic hazard scale; the embedded system is used for wirelessly transmitting video and monitoring data; and the monitoring center computer is used for predicting and forecasting the geologic hazard and comprises a communication module, a data receiving module, a rainfall and rainfall intensity-based geologic hazard prediction module, a slope displacement-time curve-based geologic hazard prediction module, a soil moisture, rainfall and rainfall intensity-based geologic hazard prediction module and a soil moisture content and slope deformation-based geologic hazard prediction module. The system has the advantages of complete monitoring means, accurate prediction and forecast, high intelligence and on-line real time.

Description

Geological hazards prediction system based on raininess, slope soil moisture content and deformation quantity
Technical field
The present invention relates to a kind of Geological Hazards Monitoring prognoses system based on technology such as the mechanics of materials, statics, dynamics, computer software and hardware, sensing detection.
Background technology
Because the polytrope of geologic media, the complicacy of geologic hazard genesis mechanism, the randomness of Spatio-temporal Evolution and process sudden, the mankind can't carry out prediction to geologic hazard at present effectively, and its basic reason is that the technological means that adopts does not also possess the effectively ability of perception environment.
Learn from office of the national mitigation council, the first half of the year in 2010, China's disaster situation presents characteristics such as " disaster frequent occurrences; casualty loss is huge ", this year 1 is to June, 19522 of geologic hazards occur in the whole nation, and 3514 people die in all kinds of disasteies, because of 2113.9 hundred million yuan of calamity direct economic losses.Wherein the most frequent, the disaster-stricken face of the generation of geologic hazard is the widest in the various disasteies, loss is maximum.Compared to June with 2009 1, the geologic hazard generation same period in this year quantity growth nearly 10 times.
Geologic hazard has three characteristics.The one, multi-point and wide-ranging, take slip and fall as main.The 2nd, regional strong, scale is take small-sized as main.The 3rd, take the heavy showers initiation as main.
Aspect monitoring technology, the monitoring on avalanche, landslide mainly concentrates on surface deformation monitoring, underground deformation monitoring, is out of shape relevant physical quantity monitoring with landslip, and concrete monitoring method is existing clear in " People's Republic of China's geological and mineral industry standard: avalanche, landslide, rubble flow monitoring standard ".Except above routine monitoring method, relatively the method for widespread use also has TDR deformation monitoring, GPS deformation monitoring, radio network technique to be applied to Landslide Groundwater and crack Automatic continuous dynamic monitoring at present.The Contents for Monitoring of rubble flow then mainly is divided into formation condition (solid matter source, resource of water supply etc.) monitoring, motion conditions (dynamic key element that flows, dynamic factors and defeated move punching become silted up etc.) monitoring, characteristic of fluid (material form and material resources chemical property etc.) monitoring, and concrete monitoring technology then mainly contains the automatic monitoring methods such as earthquake sounds monitoring, the monitoring of mud position and impulsive force monitoring.
(number of patent application: 200710178762.0) disclose geological calamity rainfall monitoring prealarmer, this invention provides a kind of suitable geological hazard meteorological early warning to Chinese invention patent, and is simple to operate, dustproof anti-blocking geological calamity rainfall monitoring prealarmer.It is characterized in that geologic hazard rainfall monitoring and early warning are organically combined, carry out geo-hazard early-warning according to the rainfall monitoring value of actual measurement.By liquid level reading observation rainfall value, by triangular-notch weir monitoring rainfall intensity, prevent by woven wire that foreign matter from entering and hold the rain mouth and blocking pipeline prevents that by jettison gear dust is residual.Chinese invention patent (number of patent application: 200910058195.4) disclose a kind of geologic hazard emergency monitoring prediction analysis method, by at the disposable radar responder of random even input or in the geologic hazard zone that forms of prediction, make it mobile with landslide massif or rubble flow, flood etc. on the earth's surface.By adopting radar scanning technic, fast monitored directly perceived is the running orbit of radar responder relatively, further analyze place, the time of judging formation of marine disasters in conjunction with landform, landforms situation, cooperate the out of Memory such as rainfall amount, earthquake to draw basic cause, the important informations such as the time period that the territorial scope that disaster will affect and prediction occur, safe escape route.Chinese invention patent (number of patent application: 200910241585.5) disclose geologic hazard mass presdiction and disaster prevention multifunctional ruler and measuring method thereof, this invention comprises and fixing on the ground and with measuring staff and the L-type carriage of ground rigidity rotation, the top movable on the long limit of L-type carriage has mounted the first dip stick, be provided with the second dip stick along its length direction on its minor face, also be provided with compass on the L-type carriage; The postpone length direction of measuring staff of the long limit of L-type carriage reclines to measure, so that the first dip stick is according to the change of measuring staff attitude and around the rotation that mounts of its upper end, the second dip stick is measured the scale of the first dip stick bottom and its intersection location simultaneously.The present invention comes inverse to go out domatic inclination angle by measuring length, utilizes compass to measure fracture azimuth, measures the crack relative displacement with the first dip stick and the second dip stick.Chinese invention patent (number of patent application: 200910058196.9) disclose a kind of intelligent geological calamity synthetic monitoring system and multi-stage prediction analysis method, this system is a kind of capable of being combined as required, dismounting, be suitable for the open-air monitoring device of installing, be a shaft after its installation combination is finished, deep deformation information is in the system information analysis controlling unit under Timing measurement and the terrestrial wireless emission slope land, in conjunction with adopting laser scanner, regularly continuous sweep obtains image in the monitored area, also set up simultaneously multiple spot Fixed Point Laser stadimeter, selected object is carried out precision ranging.It is take geologic hazard body underground deep distortion and the direct deformation displacement in earth's surface as main monitoring and prediction foundation, change auxiliary parameter and risk factor in conjunction with physics and chemical fields, hazard forecasting is divided into preparation level, early warning level, forecast in early stage level, hazard forecasting level Four, integrally finishes on the spot monitoring, analysis, forecast function.
Above-mentioned several disclosed patented technologies exist the problem of the following aspects at least: 1) monitoring method is more single, thereby the information when having caused the observation geologic hazard to occur is abundant not; 2) monitoring means does not take into full account the cause-effect relationship that geologic hazard occurs, thereby has caused being difficult to hold the omen that geologic hazard occurs; 3) monitoring instrument that adopts is relatively more expensive, thereby has caused being difficult to the extensive in real time dynamic geological hazards prediction forecast of implementing; 4) not yet adopt the formation mechanism of thought, the mechanics of materials, dynamics and static analytical geologic hazard of philosophy and the Occurrence Evolution process of whole geologic hazard, thereby caused being difficult to the problem such as sudden of the randomness of complicacy, Spatio-temporal Evolution of well explain geologic hazard genesis mechanism and process.The breakthrough of any one great natural science technology nearly all is to be based upon correct philosophical thinking to instruct on the basis, and it is guidance that the Prediction of geological disasters forecast equally also needs philosophical thinking.
Summary of the invention
Means are more single on the existing Geological Hazards Monitoring prediction methods, the insufficient prediction model that causes of information is not accurate in order to overcome; The relation of not considering the chain of causation that geologic hazard occurs causes cause-effect relationship unintelligible; Monitoring instrument is expensive, the more coarse geologic hazard situation that can only monitor some parts that causes of monitoring method, is difficult to hold the essence of geologic hazard from the overall situation and whole angle; The mechanism and the whole process that do not occur from geologic hazard are decomposed fully, caused be difficult to hold at the prediction of macroscopic view, middle sight and microcosmic different angles and at the aspects such as monitoring of each material time point of disaster generating process main key core point, etc. some problems, the invention provides that a kind of monitoring means is comprehensive, prediction is accurate, the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity of intelligent degree height, real-time online.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity comprises the automatic rainfall meter for the rainfall raininess of monitoring geologic hazard point, the soil moisture sensor that is used for monitoring geologic hazard point soil moisture, for detection of the earth's surface of geologic hazard point and the omnibearing tilt sensor of inner deformation, omnibearing vision sensor for assessment of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used for carrying out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and the power supply of described automatic rainfall meter; Described omnibearing vision sensor, described soil moisture sensor be connected the automatic rainfall meter and be connected with described embedded system, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rainfall meter are placed on the massif of geologic hazard origination point, described automatic rainfall meter, described omnibearing vision sensor and described embedded system are configured in the same vertical rod, and described soil moisture sensor is implanted near the depth of soils the described vertical rod;
Described omnibearing tilt sensor is configured on the massif of geologic hazard origination point, to form a power Sensor Network; Each omnibearing tilt sensor has the code clerk that place position is buried in a reflection underground; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication;
Described Surveillance center computing machine comprises:
Communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control data and Monitoring Data;
Data reception module, be used for to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and with the slope shape parameter certificate of geographical location information; Simultaneously with these data take collecting location, be that the geographic position data of geologic hazard point leaves in the multimedia database as major key; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Based on the geological hazards prediction module of rainfall raininess, in order to adopt two kinds of Monitoring Data of rainfall raininess and slope deformation, as shown in table 1; The prerequisite that adopts this module to carry out effective Accurate Prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, and table 1 is the corresponding table of early warning preventive measure in the different raininess situations between flush period:
Table 1
In the table 1, the mm/d of unit of raininess, raininess below 25mm/d be in, light rain, raininess is heavy rain between 25~49.9mm/d, raininess is heavy rain between 50~100mm/d, it is extra torrential rain that raininess surpasses 100mm/d;
Carry out the geological hazards prediction module based on displacement of inclined plane-time curve, be used for utilizing the relation of the stressed and deformation of the mechanics of materials to predict, the Slope slope of a curve utilizes grazing angle α iExpress, shown in formula (1),
α i = arctan ( T ( i ) - T ( i - 1 ) t i - t i - 1 ) = ΔT Δt - - - ( 1 )
In the formula (1), i (i=1,2,3 ..., n) be time series: α iBe the grazing angle of accumulation displacement T (i), Δ S (i) is displacement of inclined plane variable quantity in a certain unit interval section; V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment;
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Geological hazards prediction module based on soil moisture content and rainfall raininess, in order to according to geologic hazard be controlled by sliding mass from gravity and shearing strength, the method of carrying out prediction according to the water cut in the soil and deflection obtains prediction curve, as shown in Figure 6, wherein, K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content, is that moisture in the soil reaches 1.5% such as K1; K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content, is that moisture in the soil reaches 11.5% such as K5; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass;
Can know from described prediction curve, for same deflection, uppermost curve K1 shows, larger rainfall raininess threshold value just can cause geologic hazard and occur, there are K2, K3, three curves of K4 in the centre, and nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and occur; At first do a perpendicular line according to the deflection δ n that monitors in prediction curve, can obtain 5 points with 5 curve intersections, then the water cut according to the soil that detects finds and immediate 2 points of this water cut, A2 and A3; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations;
Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf.
As preferred a kind of scheme: described Surveillance center computing machine also comprises: based on the geological hazards prediction module of soil moisture content and Slope amount, be used for utilizing the information such as rate of change of rate of change, Slope amount and the Slope amount of soil moisture content, soil moisture content; The variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is upper monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is upper monitoring soil moisture content constantly, and Δ t is twice detection interval time constantly;
The stress of sliding mass and the computing method of stress rate represent with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is for by the sliding mass adaptability to changes that produces of moisture content part only, Δ σ (i) is for by the sliding mass variable quantity of the adaptability to changes that produces of moisture content part only;
The relation that represents resistance to shear of soil and strain with formula (5);
K1(i)=Δσ(i)/Δδ(i)=w×sinα×ΔH(i)/D×Δδ(i)=k×ΔH(i)/Δδ(i) (5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/D (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
Know that from the equation of formula (7) size of K (i) value depends primarily on water cut the soil and the changing value of displacement of inclined plane amount; With the judgement of comparing of ratio K (i) value of the rate of change of stress and strain and several mechanics control threshold value, the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and Slope amount data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the Slope amount data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, then use formula (7) calculating based on the ratio K (i) of the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be at present plastic period if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
In the above-mentioned algorithm, KH1 is the control threshold value of the variable quantity of soil moisture content, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns.
As preferred another kind of scheme: described Surveillance center computing machine also comprises: the decision-making supplementary module, be used for the result of determination of above-mentioned 4 kinds of prediction module is carried out comprehensively, it is auxiliary to adopt average weighted mode to make a strategic decision, the weights coefficient of described geological hazards prediction module based on the rainfall raininess is respectively 0.5, the described weights coefficient that carries out the geological hazards prediction module based on displacement of inclined plane-time curve is respectively 1.05, the weights coefficient of described geological hazards prediction module based on soil moisture content and rainfall raininess is respectively 1.75, the weights coefficient of described geological hazards prediction module based on soil moisture content and Slope amount is respectively 1.2, quantized value with blue early warning is defined as 3 simultaneously, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculate final comprehensive judged result with formula (8)
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV) (8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module based on the rainfall raininess and predict the outcome and weight coefficient R IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module based on soil moisture content and rainfall raininess and predict the outcome and weight coefficient R IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and Slope amount predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, final judge result of calculation with the early warning color, report, check on and sign and issue management and summarize with table 3, table 3 aid decision making and early warning signal are issued flow process:
Table 3
Further, described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system adopt rainfall Event triggered mode, when rainy, contact switch by the automatic rainfall meter activates described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system, so that they enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not occur under the geology disaster scenarios it and all monitoring devices all enter dormant state;
Described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery satisfies described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours.
The 3G wireless communications mode is adopted in message exchange between described embedded system and the described Surveillance center computing machine.
Described omnibearing tilt sensor comprises cone, conical conductive coil, insulated wire, lower cone, wire, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury; The size of pyramid type conductive coil is big or small identical with upper cone inside, coil by four large minor radius of difference in the conical conductive coil consists of a cone with same center of circle vertical pile, namely use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is Δ, and consisting of between each coil of conical conductive coil is not conducting, each coil has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the edge of the pyramid type conductive coil after the embedding is Δ from the distance of the bottom of upper cone, be welded on respectively loop A with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of lower cone inserts the conduction hollow tubular, one section inside that enters lower cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to the outside of lower cone, insert cementing agent so that conduction hollow tubular and lower cone are fixed and sealed, the inside of insulated wire and lower cone and conduction hollow tubular keep state of insulation, in lower cone, add mercury, the capacity of mercury just in time fills up lower cone, then upper cone and lower cone are fixed together and form airtight space, with shell whole omnibearing tilt sensor is fixed together at last; Upper cone and lower cone adopt the transparent plastic compacting to form, go between respectively on the shell with the conduction hollow tubular wire A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core wire among wire A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury of lower cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore if the inclination that a certain degree appears in any one orientation all can so that a certain core or multicore cable among wire A and the wire B communicate; The method of judging the angle of inclination is: if communicate between wire A and the D coil just can judge at this moment inclination angle more than 2 ° or 2 °; If do not communicate between wire A and the D coil, the phase general rule judges that the angle that at this moment tilts is between 1.5 ° to 2 ° between wire A and the coil C; If do not communicate between wire A and D coil and the C coil, the phase general rule judges that the angle that at this moment tilts is between 1 ° to 1.5 ° between wire A and the coil B; If wire A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If wire A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with the input of described 5 lines as the data acquisition end of described less radio-frequency generating unit.
Described less radio-frequency generating unit comprises MCU and radio transmitting and receiving chip, MCU is connected by spi bus with radio transmitting and receiving chip, the two consists of wireless transport module, gives described embedded system with the angle of inclination data transmission that detects, and communication mode is ZigBee technology; Described MCU is the STC89LE516AD single-chip microcomputer, is 8 single-chip microcomputers of 51 kernel enhancement mode, and is fully compatible with the IntelMCS51 series monolithic.STC89LE516AD has memory function on the abundant sheet, has 64KBFlash and 512 byte RAM.Single-chip microcomputer self is solidified with the ISP program, downloads by serial ports; Described wire A and described loop A, B, C, D all are linked into the parallel port of STC89LE516AD single-chip microcomputer, with the conducting between described wire A and the described loop A as exterior interrupt, only wake described less radio-frequency generating unit in the conducting situation between described wire A and the described loop A up, described less radio-frequency generating unit is in the park mode state when not tilting to occur;
Single-chip microcomputer at omnibearing tilt sensor comprises initialize routine module, trace routine module and launching procedure module, and the initialize routine module is processed single-chip microcomputer, radio frequency chip, SPI etc.; The trace routine module detects and judges the pitch angle, and the pitch angle data and the detection time that detect are packed with the information such as code clerk that place position is buried in reflection underground; The launching procedure module is delivered to the output of radio frequency generation module with the packet of setting up by single-chip microcomputer SPI interface; Described embedded system receives the Monitoring Data that radio frequency generation module sends over from described omnibearing tilt sensor, and gives described Surveillance center computing machine with these data transmission.
Described automatic rainfall meter obtains every day and the data of rainfall raininess per hour, and data are transferred to described Surveillance center computing machine in real time.
Described soil moisture sensor adopts Capacity Soil Moisture Sensor, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be at ordinary times dormant state, when rainy, just trigger work, can obtain water cut data in the soil by Capacity Soil Moisture Sensor, and these data are transferred to described Surveillance center computing machine in real time.
Technical conceive of the present invention is: crack this geological hazards prediction and forecast a global difficult problem, must be from formation mechanics of geological hazards, from the breeding of whole geologic hazard, development with the Occurrence Evolution process is set about, the internal cause that occurs from geologic hazard and external cause relation, the ubiquity and singularity relation, the principle from quantitative change to qualitative change that occur from geologic hazard are carried out comprehensive analysis-by-synthesis, from Geological Hazards Monitoring prediction angle hold have ubiquity, the core key issue.
The philosophical thinking of quantitative change and qualitative change is for our enlightenment: 1) generation of geologic hazard is inevitable, and the human only thing that can do is how effectively to take precautions against natural calamities, keep away calamity, mitigation and the disaster relief; 2) will carry out prediction to geologic hazard and just must accurately hold degree when causing the geologic hazard qualitative change, the monitoring means of science and the degree when setting qualitative change exactly according to local concrete condition are very important; 3) from the viewpoint of systematology, geologic hazard belongs to the critical open system of a kind of self-organization, and the identification of the critical conditions (self-organization critical characteristic) when qualitative change occurs is very crucial.
The philosophical thinking of internal cause and external cause relation is for our enlightenment: the element task of 1) investigating out geologic hazard point accurately is very important, must investigate further from situations such as geology, landforms, the hydrology, vegetation, soil, obtain related data, especially the landforms around the thickness of the angle on slope, spongiosa soil, rickle and scale and the slope; 2) external cause can cause that the variation of things matter, statistics also show sometimes, and all because heavy showers causes, the early-warning and predicting work of carrying out heavy showers at the geologic hazard point is necessary and is highly effective in extremely most geologic hazard; 3) the basis viewpoint that external causes become operative through internal causes, only existing more than the certain slope in spongiosa soil, the rickle situation, the external influences such as heavy showers so that the shearing resistance between spongiosa soil, the increase of rickle water cut and soil and the massif reduce, thereby caused the generation of geologic hazard, from this viewpoint, the size of the water cut in the soil of slope is directly related with the geologic hazard generation, and heavy showers and geologic hazard generation indirect correlation; Carrying out prediction with the water cut in the soil can be more accurate than the early-warning and predicting of heavy showers; 4) according to internal cause and the mutual viewpoint that transforms of external cause, earthquake generation, current hydraulic pressure change, historical geologic hazard generation scale and time etc., all can change or affect to a certain extent the internal cause of geologic hazard point, must consider these factors during modeling, these factors need to be summarized in the historical factor; In addition, not unalterable after the prediction model establishes, need to revise according to actual conditions.
Fasten from the pass of ubiquity and singularity, philosophy is for our enlightenment: 1) although trend, the pattern of each geologic hazard point generation geologic hazard are similar, also need the concrete condition concrete analysis for certain geologic hazard point; 2) according to the formula of " characteristics of contradiction singularity=universality of contradiction+difference and things of the like description ", at first must extract the mathematical model of the universal law of describing the geologic hazard generation, the mathematical model of this part is geostationary; Then to revise mathematical model according to " characteristics of difference and things of the like description ", to embody the singularity of each geologic hazard point; 3) internal cause of generation geologic hazard and external cause are constantly to change, internal cause and external cause during these change belongs to " characteristics of difference and things of the like description ", and the prediction mathematical model of therefore setting up also must adapt to the internal cause of generation geologic hazard and the variation of external cause; 4) the Prediction of geological disasters forecast must be held principal contradiction and the principal aspect of a contradiction, and the soil moisture content increase that the distortion of slope body and heavy showers cause is that the direct inducement of geologic hazard is exactly principal contradiction and the principal aspect of a contradiction that forms geologic hazard; The distortion of Real-Time Monitoring slope body, the moisture of sliding mass soil and rainfall raininess are the keys of carrying out the scientific forecasting forecast.
Prediction must firmly be held the degree of rainfall raininess, the degree of soil moisture content and the degree of soil deformation accurately, exists apparent causal connection between their these three degree.As a geologic hazard point, the generation of geologic hazard mainly be controlled by sliding mass from gravity and shearing strength; As a geologic hazard zone, the generation of geologic hazard not only be controlled by sliding mass from gravity and shearing strength, a situation arises also to be controlled by the geologic hazard point of other direct neighbors, especially the geologic hazard situation that occurs of the top on slope; Therefore, also must fully take into account connecting each other between the chain of causation of geologic hazard, 1. from the slope body soil moisture of the heavy showers of direct inducement → 2. increase → 3. deadweight increase and the shearing resistance between slope body and the massif of slope body reduce → 4. downward landing power and the shearing resistance of slope body be in any one little disturbance of the instability status of critical conditions → 5. slope body generation plastic yield → 6. → 7. will the weakest point (being in the point of critical conditions) cause the landslide of this point of the slope body of landslide Landslide Hazards → 8. and the division of massif failure → 9. come down cause adjacent slopes body unstability affect near on every side near the slope body of critical conditions, break out large-scale geologic hazard (being similar to the sandy beach model); Generating process and the chain of causation with geologic hazard among the present invention are decomposed into above-mentioned 9 nodes, in order to realize realizing accurately the Prediction of geological disasters forecast, each node on the chain of causation that must occur from extensive geology disaster is set up corresponding mechanical model, for the geological hazards prediction forecast of event-driven and model-driven is provided fundamental basis.
Beneficial effect of the present invention is: by setting up a kind of Serious geological disasters emergency disposal system and command system based on the geological hazards prediction forecasting procedure of raininess, slope soil moisture content and deformation quantity, realize fast, efficient, science, dispose Serious geological disasters in an orderly manner, reduce to greatest extent the loss that geologic hazard causes the mankind.So that the masses that threatened by geologic hazard can improve the ability of " Urine scent, self-monitoring, self-forecast, self-precaution, oneself meet an urgent need and oneself's treatment "; So that the level of " fast investigation, fast monitoring, soon qualitative, fast demonstration, soon decision-making and fast enforcement " can improve in government decision commander department.
Description of drawings
Fig. 1 is the internal and external reasons graph of a relation based on the geological hazards prediction forecast analysis geologic hazard generation of raininess, slope soil moisture content and deformation quantity;
Fig. 2 is the mechanical model that geologic hazard occurs;
Fig. 3 is the Occurrence Evolution process mechanic model of geologic hazard;
Fig. 4 is the several main observation station that geologic hazard occurs;
Fig. 5 is the arrangement synoptic diagram that various sensors are put in geologic hazard;
Fig. 6 is the relation curve based on the geological hazards prediction of soil moisture content and rainfall raininess;
Fig. 7 is a kind of power Sensor Network design of graphics that waits geologic hazard for the monitoring landslide;
Fig. 8 is the power Sensor Network design of graphics of the geologic hazards such as a kind of high precision monitor landslide;
Fig. 9 is a kind of synoptic diagram of automatic rainfall rainfall intensity recorder;
Figure 10 is a kind of synoptic diagram of plug-in type soil moisture sensor;
Figure 11 is a kind of synoptic diagram of omnibearing tilt sensor;
Figure 12 is the stress strain curve of the soil body;
Figure 13 is the geological hazards prediction software of forecasting system chart in Surveillance center's computing machine.
Embodiment
The invention will be further described below in conjunction with accompanying drawing.
With reference to Fig. 1~Figure 13, a kind of based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity comprises the automatic rainfall meter for the rainfall raininess of monitoring geologic hazard point, the soil moisture sensor that is used for monitoring geologic hazard point soil moisture, for detection of the earth's surface of geologic hazard point and the omnibearing tilt sensor of inner deformation, omnibearing vision sensor for assessment of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used for carrying out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and the power supply of described automatic rainfall meter; Described omnibearing vision sensor, described soil moisture sensor be connected the automatic rainfall meter and be connected with described embedded system, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rainfall meter are placed in the zone on the landslide that is not easy on the massif of geologic hazard origination point to cave in, such as more firm zone, massif basis, as shown in Figure 5, for near the settlement of convenient three sensors of line is configured in as far as possible, described automatic rainfall meter, described omnibearing vision sensor and described embedded system are configured in the same vertical rod; Described soil moisture sensor is implanted about 10CM depths near the soil of described vertical rod, and the soil of selection need to be consistent with the soil on slope, in order to can truly reflect the water cut in the soil of slope; Described omnibearing tilt sensor is configured in easy generation landslide and position, landslide according to certain distribution rule, to form a power Sensor Network, such as Fig. 7, shown in Figure 8; Each omnibearing tilt sensor has the code clerk that place position is buried in a reflection underground, just can know immediately in which locus of massif earth's surface or inner deformation have occured in case the omnibearing tilt sensor of some code clerks is subjected to displacement deformation; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication, the power Sensor Network just becomes the wireless sense network of a sensing deformation power ZigBee like this;
Communicate by letter often and the zone of electric power supply difficulty in the geologic hazard generation area, therefore need to carry out to system the design of province's power consumption, self-powered and radio communication; Because geologic hazard occurs all because heavy showers is brought out, so adopt in the present invention rainfall Event triggered mode, namely only when rainy just start-up system carry out the monitoring and warning forecast, monitoring and forecasting forecast system at ordinary times, comprise that described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system all are in dormant state, can greatly reduce like this power consumption of system; When rainy, the contact switch by the automatic rainfall meter activates described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system, so that they enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not occur under the geology disaster scenarios it and all monitoring devices all enter dormant state, to realize economizing the design of power consumption; The power supply of described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system is provided by described solar powered unit, described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery need satisfy described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours; The 3G wireless communications mode is adopted in message exchange between described embedded system and the described Surveillance center computing machine;
For with the more Slope information of minimum senser element perception, adopt omnibearing tilt sensor simultaneously earth's surface, perception slope and internal modification among the present invention, specific practice is to bury row's vertical rod underground on the ground of the geologic hazards such as landslide possibility generation area, vertical rod adopts cement steel to make, the depth of burying of vertical rod is more than 0.5 meter, the upper end of vertical rod is from about 1 meter of slope, spacing between the vertical rod is 3~5 meters, as shown in Figure 7, connect with cable between each vertical rod, it is 2cm woollen goods wire rods that cable materials adopts diameter, be connected with weaving manner between cable and the cable, aperture area between the cable is not more than 0.25 square metre, omnibearing tilt sensor is placed in the middle vertical rod of each vertical rod, if the distance of burying underground between each vertical rod surpasses more than 100 meters, considers to increase monitoring device, namely for the situation that a plurality of monitoring devices are arranged, the distance between each monitoring device is about 100 meters; Here be noted that the power Sensor Network is not to causing rocking and tilting of vertical rod in the typhoon situation below 12 grades; When the earth's surface on slope and innerly the power Sensor Network is produced acting force when deformation occurs, so that monitoring device can monitor the inclination of vertical rod, at this moment omnibearing tilt sensor work passes to embedded system by wireless sense network with deformation data;
Because earth's surface, perception slope and internal modification need a large amount of omnibearing tilt sensors that use, these omnibearing tilt sensors should satisfy can perception slope body different earth's surface and inner deformation quantities, satisfy again inclination sensor simultaneously and be cheap, low-power consumption, the requirement such as non-maintaining; Adopted in the present invention many vertical rods to add the scheme of cable, by cable each vertical rod is linked together, in case after being subject to the external force of rubble flow, landslide generation on any cables or in any vertical rod, inclination in various degree will occur in all vertical rods, thereby so that the omnibearing tilt sensor that is placed in the vertical rod perceives the inclination of vertical rod and triggers the transmission tilt data, its principle as shown in Figure 11; Described omnibearing tilt sensor comprises that cone, conical conductive coil, insulated wire, lower cone, wire, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury consist of; The size of pyramid type conductive coil is big or small identical with upper cone inside, coil by four large minor radius of difference in the conical conductive coil consists of a cone with same center of circle vertical pile, namely use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is Δ, and consisting of between each coil of conical conductive coil is not conducting, each coil has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the edge of the pyramid type conductive coil after the embedding is Δ from the distance of the bottom of upper cone, be welded on respectively loop A with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of lower cone inserts the conduction hollow tubular, one section inside that enters lower cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to the outside of lower cone, insert cementing agent so that conduction hollow tubular and lower cone are fixed and sealed, the inside of insulated wire and lower cone and conduction hollow tubular keep state of insulation, in lower cone, add mercury, the capacity of mercury just in time fills up lower cone, then upper cone and lower cone are fixed together and form airtight space, with shell whole omnibearing tilt sensor is fixed together at last; Upper cone and lower cone adopt the transparent plastic compacting to form, go between respectively on the shell with the conduction hollow tubular wire A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core wire among wire A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury of lower cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore if the inclination that a certain degree appears in any one orientation all can so that a certain core or multicore cable among wire A and the wire B communicate; The method of judging the angle of inclination is: if communicate between wire A and the D coil just can judge at this moment inclination angle more than 2 ° or 2 °; If do not communicate between wire A and the D coil, the phase general rule judges that the angle that at this moment tilts is between 1.5 ° to 2 ° between wire A and the coil C; If do not communicate between wire A and D coil and the C coil, the phase general rule judges that the angle that at this moment tilts is between 1 ° to 1.5 ° between wire A and the coil B; If wire A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If wire A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with the input of these 5 lines as the data acquisition end of described less radio-frequency generating unit 9;
Described less radio-frequency generating unit mainly comprises MCU and radio transmitting and receiving chip, MCU is connected by spi bus with radio transmitting and receiving chip, the two consists of wireless transport module, gives described embedded system with the angle of inclination data transmission that detects, and communication mode is ZigBee technology; ZigBee technology be a kind of closely, low complex degree, low-power consumption, low data rate, two-way wireless communication technology cheaply, mainly be suitable for automatically control and remote control field, can be embedded in the various device, support simultaneously geographic positioning functionality, be particularly useful for that use amount among the present invention is large, the layout scope is wide, power consumption requires the geologic hazard point earth's surface low, that the transmission of data is few and transmission range is near and the monitoring needs of internal modification;
Described MCU is the STC89LE516AD single-chip microcomputer, is 8 single-chip microcomputers of 51 kernel enhancement mode, and is fully compatible with the IntelMCS51 series monolithic.STC89LE516AD has memory function on the abundant sheet, has 64KBFlash and 512 byte RAM.Single-chip microcomputer self is solidified with the ISP program, downloads by serial ports; Described wire A and described loop A, B, C, D all are linked into the parallel port of STC89LE516AD single-chip microcomputer, with the conducting between described wire A and the described loop A as exterior interrupt, only wake described less radio-frequency generating unit in the conducting situation between described wire A and the described loop A up, described less radio-frequency generating unit is in the park mode state when not tilting to occur;
Described radio transmitting and receiving chip adopts CC2500, and it is a low cost, low-power consumption, high performance radio transmitting and receiving chip, and its working frequency range is the ISM band of 2.4GHz; Have good wireless receiving sensitivity and powerful antijamming capability; Only the stream of 0.9 μ A consumes when park mode, external interrupt or RTC energy waken system; The stream consumption that when standby mode, is less than 0.6 μ A, external interrupt energy waken system; Hardware supported CSMA/CA function; Voltage is 1.8~3.6V; Under transmission mode, when output power be-during 12dBm, current drain is 12mA.The receiver susceptibility of CC2500 is-101dBm (when 10kbps); Peak power output is 0dBm, and data rate can change between 1.2kbps~500kbps; With the USART of 2 powerful several groups of agreements of support, and 16 bit timers and 28 bit timers of 1 MAC timer, 1 routine;
Mainly comprised initialize routine, trace routine and launching procedure in the software of the single-chip microcomputer of omnibearing tilt sensor, initialize routine mainly is that single-chip microcomputer, radio frequency chip, SPI etc. are processed; Trace routine mainly detects and judges the pitch angle, and the pitch angle data and the detection time that detect are packed with the information such as code clerk that place position is buried in reflection underground; Launching procedure is delivered to the output of radio frequency generation module with the packet of setting up by single-chip microcomputer SPI interface; Described embedded system receives the Monitoring Data that radio frequency generation module sends over from described omnibearing tilt sensor, and these data communication devices is crossed the 3G cordless communication network or computer communication network is transferred to described Surveillance center computing machine; Therefore, from the concept of network, several omnibearing tilt sensors and embedded system have consisted of WLAN (wireless local area network), and embedded system and Surveillance center's computing machine have consisted of wireless wide area network;
It number is 200610154671.9 the intelligence testing apparatus for precipitation rain fall based on computer vision that described automatic rainfall meter adopts Chinese invention patent application, can obtain every day and the data of rainfall raininess per hour by this equipment, and these data are transferred to described Surveillance center computing machine in real time; In general, automatic rainfall meter of configuration has satisfied the needs of Geological Hazards Monitoring basically in 2 square kilometres zone, considers from economic angle, adopts an automatic rainfall meter at a geologic hazard point;
Described soil moisture sensor adopts Capacity Soil Moisture Sensor, adopts a kind of pointer soil moisture sensor among the present invention, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be at ordinary times dormant state, only when rainy, just trigger work, can obtain water cut data in the soil by this sensor, and these data are transferred to described Surveillance center computing machine in real time; In general, pointer soil moisture sensor of configuration has satisfied the needs of Geological Hazards Monitoring basically in 2 square kilometres zone, considers from economic angle, adopts a pointer soil moisture sensor at a geologic hazard point;
Described omnibearing vision sensor adopt Chinese invention patent application number be 200610154827.3 based on the technology in the mud-stone flow disaster pick-up unit of omni-directional visual, on-the-spot panoramic picture in the time of obtaining the geologic hazard generation by this device, can assess objectively the scale of geologic hazard generation and the harm that causes, the on-the-spot panoramic video data communication device that omnibearing vision sensor gathers is crossed described embedded system and is transferred in real time described Surveillance center computing machine; In general, omnibearing vision sensor of configuration has satisfied the needs of Geological Hazards Monitoring basically in 2 square kilometres zone, considers from economic angle, at omnibearing vision sensor of the hillside of geologic hazard point configuration;
Described embedded system, the wireless sense network by ZigBee reads the angle of inclination of described omnibearing tilt sensor and the data such as coding of described omnibearing tilt sensor; Read the monitoring soil moisture value of described soil moisture sensor by the A/D interface; The analysis of image data that obtains by described omnibearing vision sensor obtains the rainfall raininess monitor value of described automatic rainfall meter; Obtain the panoramic picture at geologic hazard origination point scene by described omnibearing vision sensor; Adopt 3G wireless communication transmissions Monitoring Data between described embedded system and the described Surveillance center computing machine;
Software in the described Surveillance center computing machine comprises: communication module, data reception module, based on the geological hazards prediction module of rainfall raininess, carry out geological hazards prediction module, the geological hazards prediction module based on soil moisture content and rainfall raininess, the geological hazards prediction module based on soil moisture content and Slope amount, decision-making supplementary module, information issuing module based on displacement of inclined plane-time curve;
Described communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control data and Monitoring Data;
Described data reception module, be used for to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and with the slope shape parameter certificate of geographical location information; Simultaneously with these data take collecting location, be that the geographic position data of geologic hazard point leaves in the multimedia database as major key; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Described geological hazards prediction module based on the rainfall raininess, the data of Main Basis Slope amount and rainfall raininess data are predicted, Fig. 1 has shown the various internal and external reasons relations that geologic hazard occurs, because rainfall is the topmost external factor of induced landslide, need to pay close attention to the slope deformation in the different rainfall rank situations, take different Disposal Measures, in this module, adopted two kinds of Monitoring Data of rainfall raininess and slope deformation, as shown in table 1; Although this Forecasting Methodology has obvious causalnexus, but need to confirm with the slope shape variable, therefore it is very short to the time that the local resident takes refuge constantly to carry out early-warning and predicting at this, as shown in Figure 4, the prerequisite that adopts this module to carry out effective Accurate Prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, can with the time advance of early-warning and predicting, provide the sufficient time for keeping away calamity like this; Based on the geological hazards prediction of rainfall raininess be in fact used on the geologic hazard generation chain of causation 1., the 8. monitoring of node;
The corresponding table of early warning preventive measure in the different raininess situations between table 1 flush period
Describedly carry out the geological hazards prediction module based on displacement of inclined plane-time curve, be to utilize the relation of the stressed and deformation in the mechanics of materials to predict, material is stressed with relation deformation to be: along with the stressed material of stressed increase of material from elastic deformation, plastic yield to the process of destroying; In general, during elastic deformation, according to the Hooke theorem, the rate of deformation of material almost is a constant; During plastic yield, the rate of deformation of material increases gradually, and a distortion acceleration flex point can occur, such as the t3 point of Fig. 4; Therefore according to the slope variation characteristics in each stage of Slope curve, can adopt mathematical method quantitatively to judge.The Slope slope of a curve can utilize grazing angle α iExpress, shown in formula (1).
α i = arctan ( T ( i ) - T ( i - 1 ) t i - t i - 1 ) = ΔT Δt - - - ( 1 )
In the formula (1), i (i=1,2,3 ..., n) be time series: α iBe the grazing angle of accumulation displacement T (i), Δ S (i) is the interior displacement of inclined plane variable quantity of a certain unit interval section (generally adopting a monitoring periods, such as 1 day, 1 week etc.); V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment.
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Grazing angle α when landslide and rubble flow occur iGenerally be about 89 °.
This Forecasting Methodology is owing to just used the information of Slope, and the process of Slope amount and distortion can be accurately monitored in requirement in real time, and in addition, the signal of the deflection on slope is smaller, be easy to introduce and disturb, and can be used as a kind of supplementary means of prediction; Based on displacement of inclined plane-time curve carry out geological hazards prediction be in fact utilized on the geologic hazard generation chain of causation 5., 6., the 8. monitoring of node;
Described geological hazards prediction module based on soil moisture content and rainfall raininess, its Main Basis be the generation of geologic hazard mainly be controlled by sliding mass from gravity and shearing strength, as shown in Figure 2; What wherein the water cut in the soil namely affected sliding mass affects again the shearing strength of sliding mass from gravity, when the moisture in the soil reaches 11.5%, sliding mass from gravity can increase by 11.5% simultaneously shearing force reduce rapidly; Obviously utilize water cut and deflection in the soil to carry out the precision that prediction can improve prediction significantly; If the water cut in the soil reach 11.5% and distortion be in just boost phase, that the probability of geologic hazard occurs is just very high for this geologic hazard point so, at this moment just can be used as early-warning and predicting information publishing point; If also have in the recent period larger rainfall raininess in rainy or forecast, just should start urgent prediction scheme, the crowd that rapid evacuation may jeopardize at once; Carry out the method curve as shown in Figure 6 of prediction according to the water cut in the soil and deflection, wherein K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content, is that moisture in the soil reaches 1.5% such as K1; K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content, is that moisture in the soil reaches 11.5% such as K5; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass; Based on the geological hazards prediction of soil moisture content and rainfall raininess be in fact utilized on the geologic hazard chain of causation 1., 2., 3., 4., 5., 6., the 8. monitoring of node;
Can know that from the curve of Fig. 6 for same deflection, uppermost curve K1 shows that larger rainfall raininess threshold value just can cause geologic hazard and occur, nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and occur; A lot of geologic hazards occur well to explain above-mentioned prediction curve correctness, the geologic hazard that causes such as light rain after the snow melt and a light rain after a few days ago raining heavily; In this forecast model, the monitoring that to bring out in time external cause-raininess that geologic hazard occurs converts the internal cause monitoring soil from the soil moisture content of gravity and shearing strength that affects sliding mass to direct to, since the generation of geologic hazard mainly be controlled by sliding mass from gravity and shearing strength, hold this main key core problem and can effectively improve precision of prediction; The algorithm of prediction is as follows: at first do a perpendicular line according to the deflection δ n that monitors in Fig. 6, can obtain 5 points with 5 curve intersections, then the water cut according to the soil that detects finds and immediate 2 points of this water cut, A2 and A3; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations; Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf;
Described geological hazards prediction module based on soil moisture content and Slope amount, the description of soil stress-strain stress relation is the key problem of the various mechanical properties of the research soil body, as shown in figure 12, soil moisture content is a physical quantity directly related with soil stress, the Slope amount is again the amount directly related with the strain of soil, and the main mathematical model of the ess-strain of the soil body that adopts is predicted geologic hazard in this module; In this prediction module, utilize the information such as rate of change of rate of change, Slope amount and the Slope amount of soil moisture content, soil moisture content; The quick increase of the rate of change of soil moisture content must increase the possibility that geologic hazard occurs, the acceleration of the rate of change of same Slope amount also is the omen that geologic hazard occurs, viewpoint from mechanics, geologic hazard occurs must experience the elastic deformation of the soil body, the stage such as plastic yield and destruction, to can make a prediction accurately in early days, just need to hold accurately the A-stage of the plastic yield of the soil body, therefore this prediction module is a kind of Forecasting Methodology of utilizing static prediction and performance prediction to combine, so-called static prediction is mainly carried out analyses and prediction by the relation curve of ess-strain, and so-called performance prediction is then mainly predicted its development tendency by the rate of change of ess-strain; Therefore, at first to calculate the rate of change of soil moisture content, the rate of change of Slope amount according to soil moisture content, the Slope amount of the soil moisture content that is recorded in this geologic hazard point in the multimedia database, Slope amount and current this geologic hazard point that monitors, then comprehensively judge in conjunction with soil moisture content, the Slope amount of current this geologic hazard point that monitors; Can obtain soil moisture content and Slope the relationship between quantities curve for each ground particle, the soil moisture content increase can cause that the deadweight of sliding mass increases, the sliding force size of sliding mass is calculated with the sine value of ramp angles and the product of sliding mass deadweight, and the variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is upper monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is upper monitoring soil moisture content constantly, and Δ t was twice detection interval time constantly, such as 10 minutes, 1 hour and 1 day; Use Δ δ among the present invention 10min(i) represent sometime 10 minutes displacement of inclined plane variable quantity of section, Δ δ 1h(i) represent sometime 1 hour displacement of inclined plane variable quantity of section, Δ δ 1day(i) represent sometime 1 day displacement of inclined plane variable quantity of section, Δ H 10min(i) be the variable quantity of 10 minutes soil moisture contents of section sometime, Δ H 1h(i) be the variable quantity of 1 hour soil moisture content of section sometime, Δ H 1day(i) be the variable quantity of 1 day soil moisture content of section sometime;
The stress of sliding mass is that the product two parts by the sine that contains water inventory and value of slope of the sum of products sliding mass of the sine of the deadweight of sliding mass and value of slope are formed by stacking, and the former is determined by local geology and geomorphology environment, is geostationary; The latter determines by local geology and geomorphology environment and hydrometeorological condition are common, and mainly the variation by the water cut of soil in the sliding mass changes; The stress of sliding mass and the computing method of stress rate represent with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is for by the sliding mass adaptability to changes that produces of moisture content part only, Δ σ (i) is for by the sliding mass variable quantity of the adaptability to changes that produces of moisture content part only;
The relation that represents resistance to shear of soil and strain with formula (5);
K1(i)=Δσ(i)/Δδ(i)=w×sinα×ΔH(i)×Δδ(i)=k×ΔH(i)/Δδ(i) (5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/n (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
The calculated value of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content can select 10 minutes, 1 hour and 1 day to be the time sampling interval as required, for 10 minutes Δ δ 10min(i) and Δ H 10min(i), the undesired signal in the Monitoring Data considers to adopt the mode of single order digital low-pass filtering to eliminate internal disturbance and external disturbance disturbs;
Linear between the stress and strain in the material after solid material is stressed according to the Hooke theorem, be δ interval situation below 1 such as deflection among Figure 12, basically be one more greatly on the occasion of constant, can know that from the equation of formula (7) size of K (i) value depends primarily on water cut the soil and the changing value of displacement of inclined plane amount;
Along with presenting plasticity, stressed increase material changes, be the situation in δ 1~δ 5 intervals such as deflection among Figure 12, trend obviously appears reducing in K (i) value, situation close to zero or negative value appears sometimes, therefore the principal character of soil body generation plastic yield that Here it is can utilize this feature to carry out Prediction of geological disasters;
As shown in figure 12, when deflection reached δ 5 above situation, at this moment initial geologic hazard origination point had just appearred in the material failure;
Simultaneously we also notice when the moisture in the soil reaches 11.5%, can the increasing by 11.5% while shearing force from gravity and reduce rapidly this phenomenon of sliding mass, and its stressed graph of a relation is as shown in Figure 2; In static prediction we with the water cut in the soil as outline, such as this point of the H2 in Figure 12 be exactly water cut be 11.5%; Then we control the threshold value judgement of comparing with ratio K (i) value of the rate of change of stress and strain and several mechanics, and the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and Slope amount data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the Slope amount data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, then use formula (7) calculating based on the ratio K (i) of the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be at present plastic period if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
KH1 is the control threshold value of the variable quantity of soil moisture content in the above-mentioned algorithm, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns;
Described geological hazards prediction module based on soil moisture content and Slope amount be in fact utilized on the geologic hazard chain of causation 2., 5., 6., the 8. monitoring of node;
Described decision-making supplementary module, be used for above-mentioned multiple geological hazards prediction method is carried out comprehensively, above-mentioned four kinds of Forecasting Methodologies are carried out prediction from different aspects, as shown in figure 13, it is different that each prediction algorithm stresses face, be similar to the prediction judgement that a plurality of experts make, the result of four kinds of predictions can be consistent under certain conditions, but also different predicting the outcome can appear, therefore we have adopted the mode of information fusion, it is auxiliary namely to adopt average weighted mode to make a strategic decision, and the node on the geologic hazard chain of causation considers, in general, the precision of the more predictions of interstitial content of considering will be higher, the node location more precision by rear prediction is higher, and the present invention has designed the weights that the supplementary table of making a strategic decision obtains each module according to this thought, and is as shown in table 2;
The weights coefficient of the various prediction module of table 2
Described in the table 2 based on the geological hazards prediction module of rainfall raininess with the I type, describedly carry out the geological hazards prediction module based on displacement of inclined plane-time curve and represent with the IV type with III type, described geological hazards prediction module based on soil moisture content and Slope amount with II type, described geological hazards prediction module based on soil moisture content and rainfall raininess; Their weights coefficient is respectively 0.5,1.05,1.75 and 1.2, simultaneously we with the quantized value of blue early warning be defined as 3, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculate final comprehensive judged result with formula (8)
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV)(8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module based on the rainfall raininess and predict the outcome and weight coefficient R IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module based on soil moisture content and rainfall raininess and predict the outcome and weight coefficient R IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and Slope amount predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, will finally judge among the present invention result of calculation with the early warning color, report, check on and sign and issue to manage and summarize with table 3;
Table 3 aid decision making and early warning signal issue flow process
Fig. 3 is the sandy beach model that extensive geology disaster occurs, because the complicated geology of geologic hazard point, the ramp angles of each section massif is different, and the heavy black line among Fig. 3 represents the gradient of each section of massif; The down sliding of sliding mass 1 can increase the sliding force 2 of sliding mass 2, the downslide of sliding mass 3 equally also can reduce the shearing force 2 of sliding mass 2, so just formed a chain of causation, the generation of geologic hazard then often begins from the weakest place, all can affect the stable of its sliding mass of being correlated with up and down in case gliding appears in the some sliding masses on the chain of causation, the generation of therefore large-scale geologic hazard must be from slip occurs in the localized landslip body.Because the complicacy of the geologic media of geologic hazard point can cause the time of geologic hazard generation and the randomness in space; The tendency that will accurately and timely hold the geologic hazard generation must be monitored the generation of various mishaps, so arrange that at geologic hazard point the deformation of each sliding mass of monitoring and the relation of finding out between each sliding mass all are very important, the spatial relationship that express between each sliding mass must adopt the GIS technology;
According to the geologic hazard influence degree, Responsibility of Staffs at different levels in the early warning signal issue flow process: (1) person on duty thinks when reaching issue, change, releasing early warning signal condition, should in time report to the gaffer forecaster; Report content has comprised the spatial position data of the predicting the outcome of each prediction module, several fundamental surveillance data and prediction geologic hazard point; (2) gaffer's technology of being responsible for issuing, change, remove early warning signal is checked on, and proposes the issue suggestion, signs and issues or reports step by step according to all kinds of rank responsibilities of table 3; (3) after director on duty receives report, be responsible for holding a conference or consultation with the gaffer, propose the issue suggestion, sign and issue or report step by step according to all kinds of rank responsibilities of following table; (4) forecast is comprehensively issued suggestion after the department director receives report, signs and issues or reports step by step according to all kinds of rank responsibilities of table 3.Because the emergentness that geologic hazard occurs, what occur is greatly destructive, the early-warning and predicting that sends within the very first time of racing against time is of great significance keeping away calamity mitigation tool, early warning information need to be delivered to the related personnel as soon as possible, any related personnel can both directly see the prediction address content that described decision-making supplementary module has been done by network as a reference, and the early warning signal that official makes need to be by the issue flow process shown in the table 3;
Described information issuing module comprises for issuing the information issue of flow process towards decision-making related personnel's inside early warning signal and being used for towards the information issue of the early-warning and predicting in the external world; The information issue is issued by the mode of network platform webpage, but related personnel at different levels can only see the content of own authority, in addition for the issue of other information of yellow early warning higher level, the mode by short message sends to the responsible person concerned, in order in time tackled processing within the very first time.

Claims (9)

1. one kind based on rainfall, the geological hazards prediction system of slope soil moisture content and deformation quantity is characterized in that: described prognoses system comprises the automatic rainfall meter for the rainfall raininess of monitoring geologic hazard point, the soil moisture sensor that is used for monitoring geologic hazard point soil moisture, for detection of the earth's surface of geologic hazard point and the omnibearing tilt sensor of inner deformation, omnibearing vision sensor for assessment of geologic hazard generation scale, the embedded system that is used for wireless transmission video and various Monitoring Data, be used for carrying out Surveillance center's computing machine of geological hazards prediction forecast, be used for described omnibearing vision sensor, the solar powered unit of described soil moisture sensor and the power supply of described automatic rainfall meter; Described omnibearing vision sensor, described soil moisture sensor be connected the automatic rainfall meter and be connected with described embedded system, described embedded system is connected with the Surveillance center computing machine by communication, described omnibearing vision sensor, described soil moisture sensor and described automatic rainfall meter are placed on the massif of geologic hazard origination point, described automatic rainfall meter, described omnibearing vision sensor and described embedded system are configured in the same vertical rod, and described soil moisture sensor is implanted near the depth of soils the described vertical rod;
Described omnibearing tilt sensor is configured on the massif spongiosa soil of geologic hazard origination point, to form a power Sensor Network; Each omnibearing tilt sensor has the code clerk that place position is buried in a reflection underground; Between each omnibearing tilt sensor and and described embedded system between adopt the mode of radio communication;
Described Surveillance center computing machine comprises:
Communication module is used for carrying out radio communication and various computer network communication based on the 3G wireless communication protocol with described embedded system, receives and transmits various control data and Monitoring Data;
Data reception module, be used for to receive the various Monitoring Data that send from described embedded system, described Monitoring Data comprises rainfall raininess data, soil moisture data, on-the-spot panoramic video data and with the slope shape parameter certificate of geographical location information; Simultaneously with these data take collecting location, be that the geographic position data of geologic hazard point leaves in the multimedia database as major key; The geographic position data of geologic hazard point is named with the GPS locator data of this geologic hazard point; Implanting the geographic position data of the omnibearing tilt sensor in the slope names with the GPS locator data of burying underground a little;
Based on the geological hazards prediction module of rainfall raininess, in order to adopt two kinds of Monitoring Data of rainfall raininess and slope deformation, as shown in table 1; The prerequisite that adopts this module to carry out effective Accurate Prediction is to have understood fully that this geologic hazard point breaks out the critical rainfall amount of rubble flow, and table 1 is the corresponding table of early warning preventive measure in the different raininess situations between flush period:
Table 1;
In the table 1, the mm/d of unit of raininess, raininess below 25mm/d be in, light rain, raininess is heavy rain between 25~49.9mm/d, raininess is heavy rain between 50~100mm/d, it is extra torrential rain that raininess surpasses 100mm/d;
Carry out the geological hazards prediction module based on displacement of inclined plane-time curve, predict that for the relation of the stressed and deformation that utilizes the mechanics of materials Slope slope of a curve utilizes grazing angle α iExpress, shown in formula (1),
In the formula (1), i is time series, i=1, and 2,3 ..., n: α iBe the grazing angle of accumulation displacement T (i), Δ S (i) is displacement of inclined plane variable quantity in a certain unit interval section; V is the rate of displacement of constant speed deformation stage; T (i) for after the conversion with the ordinate value of identical dimension of time; t iBe a certain monitoring moment;
Judge according to formula (1) result of calculation and by following condition,
Work as α iThe slope is in the initial deformation stage in the time of<45 °;
Work as α iThe slope is in the constant speed deformation stage during 45 ° of ≈;
45 °<α iBe first boost phase in the time of<80 °, send blue early warning;
80 °≤α iBe middle boost phase in the time of<85 °, send orange early warning;
α iFor facing the sliding stage, send red early warning in the time of 〉=85 °;
Geological hazards prediction module based on soil moisture content and rainfall raininess, in order to according to geologic hazard be controlled by sliding mass from gravity and shearing strength, the method of carrying out prediction according to the water cut in the soil and deflection obtains prediction curve, wherein, K1 is the raininess-deformation geological hazards prediction curve of less soil moisture content; There are K2, K3, three curves of K4 in the centre, and K5 is the raininess-deformation geological hazards prediction curve of more soil moisture content; Ordinate represents that rainfall raininess value predicted value, horizontal ordinate represent the deflection of sliding mass;
Can know from described prediction curve, for same deflection, uppermost curve K1 shows, larger rainfall raininess threshold value just can cause geologic hazard and occur, there are K2, K3, three curves of K4 in the centre, and nethermost curve K5 shows that then less rainfall raininess threshold value will cause geologic hazard and occur; At first do a perpendicular line according to the deflection δ n that monitors in prediction curve, can obtain 5 points with 5 curve intersections, then the water cut according to the soil that detects finds and immediate 2 points of this water cut, A2 and A3; Obtain 1 point on perpendicular line, An according to the mode of linear interpolation; The ordinate value of this An is exactly critical raininess prediction threshold value Yn, carries out different emergency measures according to the rainfall raininess value of weather forecast; Because the deflection δ n of sliding mass and the water cut An in the soil are constantly changing, the algorithm of prediction is continuous cycle calculations;
Judgment mode is that forecast raininess value Yf predicts that with present critical raininess threshold value Yn compares, and sends blue early warning when 50%Yn≤Yf<75%Yn, sends orange early warning when 75%Yn≤Yf<100%Yn, sends red early warning when Yn≤Yf.
2. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1, it is characterized in that: described Surveillance center computing machine also comprises:
Based on the geological hazards prediction module of soil moisture content and Slope amount, for the rate of change information of rate of change, Slope amount and the Slope amount of utilizing soil moisture content, soil moisture content; The variable quantity of displacement of inclined plane variable quantity and soil moisture content calculates with formula (2), (3);
Δδ(i)=(δ(i)-δ(i-1))/Δt (2)
ΔH(i)=(H(i)-H(i-1))/Δt (3)
Δ δ (i) is displacement of inclined plane variable quantity in a certain unit interval section, δ (i) is existing monitoring displacement of inclined plane amount constantly, δ (i-1) is upper monitoring displacement of inclined plane amount constantly, Δ H (i) is the variable quantity of soil moisture content in a certain unit interval section, H (i) is existing monitoring soil moisture content constantly, H (i-1) is upper monitoring soil moisture content constantly, and Δ t is twice detection interval time constantly;
The stress of sliding mass and the computing method of stress rate represent with formula (4);
σ(i)=w×sinα×(1+H(i))/D (4)
Δσ(i)=w×sinα×ΔH(i)/D
In the formula, σ (i) is the adaptability to changes of sliding mass, w * sin α/D is the adaptability to changes that is produced by the deadweight of sliding mass moisture-free, w is the moisture free deadweight of sliding mass, α is the angle on the slope at sliding mass place, D be sliding mass perpendicular to the minimum sectional area on the stress direction, w * sin α * H (i)/D is for by the sliding mass adaptability to changes that produces of moisture content part only, Δ σ (i) is for by the sliding mass variable quantity of the adaptability to changes that produces of moisture content part only;
The relation that represents resistance to shear of soil and strain with formula (5);
k1(i)=Δσ(i)/Δδ(i)=w×sinα×Δh(I)/[D×Δδ(i)] (5)
In the formula, k uses formula (6) expression near a constant,
k=w×sinα/D (6)
For the ease of calculating, we are rewritten into formula (5) form of formula (7);
K(i)=K1(i)/k=ΔH(i)/Δδ(i) (7)
Know that from the equation of formula (7) size of K (i) value depends primarily on water cut the soil and the changing value of displacement of inclined plane amount; With the judgement of comparing of ratio K (i) value of the rate of change of stress and strain and several mechanics control threshold value, the main flow process of algorithm is as follows;
Step 1: read current soil moisture content and Slope amount data, these data are saved in the multimedia database;
Step 2: judge whether soil moisture content reaches 11.5%, and situation about not reaching forwards step 1 to;
Step 3: the soil moisture content and the Slope amount data that read the previous time, with the variation delta H (i) of formula (2), (3) calculating displacement of inclined plane variation delta δ (i) and soil moisture content, then use formula (7) calculating based on the ratio K (i) of the stress and strain of the variation delta H (i) of displacement of inclined plane variation delta δ (i) and soil moisture content;
Step 4: compare according to the ratio K (i) of stress and strain and several mechanics control threshold values, if K (i) 〉=KV1 then judge the elastic deformation stage that is at present; If KV1>K (i) 〉=KV2 then judge and be at present plastic period if the variation delta H of soil moisture content (i) 〉=KH1 sends orange early warning information, otherwise sends blue early warning information; If KV2>K (i) 〉=KV3 then judge is in plastic yield at present to the transition stage that destroys, at this moment send red early warning information; Forward step 1 to;
In the above-mentioned algorithm, KH1 is the control threshold value of the variable quantity of soil moisture content, and KV1, KV2, KV3 are respectively mechanics control threshold value, and satisfies the following KV1>KV2>KV3 that concerns.
3. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 2, it is characterized in that: described Surveillance center computing machine also comprises:
The decision-making supplementary module, be used for the result of determination of above-mentioned 4 kinds of prediction module is carried out comprehensively, it is auxiliary to adopt average weighted mode to make a strategic decision, the weight coefficient of described geological hazards prediction module based on the rainfall raininess is respectively 0.5, the described weight coefficient that carries out the geological hazards prediction module based on displacement of inclined plane-time curve is respectively 1.05, the weight coefficient of described geological hazards prediction module based on soil moisture content and rainfall raininess is respectively 1.75, the weight coefficient of described geological hazards prediction module based on soil moisture content and Slope amount is respectively 1.2, quantized value with blue early warning is defined as 3 simultaneously, the quantized value of orange early warning is defined as 6, the quantized value of red early warning is defined as 9, calculate final comprehensive judged result with formula (8)
R=(R I×K I+R II×K II+R III×K III+R IV×K IV)/(K I+K II+K III+K IV) (8)
In the formula, R IAnd K IBe respectively described geological hazards prediction module based on the rainfall raininess and predict the outcome and weight coefficient R IIAnd K IIBeing respectively described carries out the geological hazards prediction module based on displacement of inclined plane-time curve and predicts the outcome and weight coefficient R IIIAnd K IIIBe respectively described geological hazards prediction module based on soil moisture content and rainfall raininess and predict the outcome and weight coefficient R IVAnd K IVBeing respectively described geological hazards prediction module based on soil moisture content and Slope amount predicts the outcome and weight coefficient, R is the final comprehensive result of calculation of judging, the numerical range of judging result of calculation is 0~9, final judge result of calculation with the early warning color, report, check on and sign and issue management and summarize with table 3, table 3 aid decision making and early warning signal are issued flow process:
The result of calculation value Early warning face On duty The gaffer Director The supervisor deputy bureau director The chief
Look The member 0~2.90 White Report Sign and issue 2.91~4.50 Blue Report Report/check on Sign and issue 4.51~6.00 Yellow Report Report/check on Report/check on Sign and issue 6.01~7.50 Orange Report Report/check on Report/check on Sign and issue 7.51~9.00 Red Report Report/check on Report/check on Report/check on Sign and issue
Table 3
4. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system adopt rainfall Event triggered mode, when rainy, contact switch by the automatic rainfall meter activates described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and described embedded system, so that they enter duty immediately; Rose in 72 hours after rain finishing, if the monitoring and forecasting forecast system does not occur under the geology disaster scenarios it and all monitoring devices all enter dormant state.
5. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described solar powered unit is made of solar energy photoelectric conversion plate, rechargeable battery and charging circuit, and the capacity of described rechargeable battery satisfies described automatic rainfall meter, described omnibearing vision sensor, described soil moisture sensor and the working time of described embedded system more than 120 hours.
6. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2 is characterized in that: the message exchange employing 3G wireless communications mode between described embedded system and the described Surveillance center computing machine.
7. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described omnibearing tilt sensor comprises cone, conical conductive coil, insulated wire, lower cone, wire, conduction hollow tubular, shell, less radio-frequency generating unit, power supply and mercury; The size of pyramid type conductive coil is big or small identical with upper cone inside, coil by four large minor radius of difference in the conical conductive coil consists of a cone with same center of circle vertical pile, namely use loop A, coil B, coil C and coil D are superimposed as a taper shape, distance between each coil is Δ, and consisting of between each coil of conical conductive coil is not conducting, each coil has an extension line to lead to the outside of cone, the pyramid type conductive coil is embedded in the cone, the edge of the pyramid type conductive coil after the embedding is Δ from the distance of the bottom of upper cone, be welded on respectively loop A with 4 core insulation lines respectively, coil B, on the extension line of coil C and coil D, the epiconus place of lower cone 4 inserts the conduction hollow tubular, one section inside that enters lower cone of conduction hollow tubular, insulated wire passes the inside of conduction hollow tubular and insulated wire is guided to the outside of lower cone, insert cementing agent so that conduction hollow tubular and lower cone are fixed and sealed, the inside of insulated wire and lower cone and conduction hollow tubular keep state of insulation, in lower cone, add mercury, the capacity of mercury just in time fills up lower cone, then upper cone and lower cone are fixed together and form airtight space, with shell whole omnibearing tilt sensor is fixed together at last; Upper cone and lower cone adopt the transparent plastic compacting to form, go between respectively on the shell with the conduction hollow tubular wire A that is connected and the quad B that is connected with insulated wire, when omnibearing tilt sensor did not perceive, any core wire among wire A and the quad B did not communicate; When omnibearing tilt sensor has perceived inclination, some flow in the cone at the mercury of lower cone inside, and be embedded in cone in conical conductive coil in coil contact, when being 0.5 °, the angle of inclination contacts with loop A, when being 1 °, the angle of inclination contacts with loop A and coil B, when the angle of inclination is 1.5 ° and loop A, coil B and coil C contact, contact with all coil when the angle of inclination is 2 °, therefore if the inclination that a certain degree appears in any one orientation all can so that a certain core or multicore cable among wire A and the wire B communicate; The method of judging the angle of inclination is: if communicate between wire A and the D coil just can judge at this moment inclination angle more than 2 ° or 2 °; If do not communicate between wire A and the D coil, the phase general rule judges that the angle that at this moment tilts is between 1.5 ° to 2 ° between wire A and the coil C; If do not communicate between wire A and D coil and the C coil, the phase general rule judges that the angle that at this moment tilts is between 1 ° to 1.5 ° between wire A and the coil B; If wire A only and between the loop A conducting judge that then the angle that at this moment tilts is between 0.5 ° to 1 °; If wire A and any coil not conducting judge that then the angle of inclination is below 0.5 °; Among the present invention with the input of described 5 lines as the data acquisition end of described less radio-frequency generating unit.
8. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described automatic rainfall meter obtains every day and the data of rainfall raininess per hour, and data are transferred to described Surveillance center computing machine in real time.
9. the geological hazards prediction system based on rainfall, slope soil moisture content and deformation quantity as claimed in claim 1 or 2, it is characterized in that: described soil moisture sensor adopts Capacity Soil Moisture Sensor, described soil moisture sensor vertically is inserted into the 10CM depths of spongiosa soil, rickle; Be at ordinary times dormant state, when rainy, just trigger work, can obtain water cut data in the soil by Capacity Soil Moisture Sensor, and these data are transferred to described Surveillance center computing machine in real time.
CN2010102979456A 2010-09-28 2010-09-28 System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation Active CN102013150B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2010102979456A CN102013150B (en) 2010-09-28 2010-09-28 System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2010102979456A CN102013150B (en) 2010-09-28 2010-09-28 System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation

Publications (2)

Publication Number Publication Date
CN102013150A CN102013150A (en) 2011-04-13
CN102013150B true CN102013150B (en) 2013-03-27

Family

ID=43843309

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2010102979456A Active CN102013150B (en) 2010-09-28 2010-09-28 System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation

Country Status (1)

Country Link
CN (1) CN102013150B (en)

Families Citing this family (54)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102169617B (en) * 2011-04-15 2013-02-20 中国科学院水利部成都山地灾害与环境研究所 Early warning method of mud-rock flow used in regions short of rainfall data
CN102789674B (en) * 2011-05-17 2014-07-02 宝鸡市博远信航电子科技有限责任公司 Geological disaster networking monitoring pre-warning system
CN102306233B (en) * 2011-06-15 2014-04-16 浙江大学 River basin landslide space-time predicting method under rainfall effect
CN103077584A (en) * 2011-10-25 2013-05-01 成都玺汇科技有限公司 Geological disaster monitoring and early warning system
CN102419892A (en) * 2011-11-21 2012-04-18 航天科工深圳(集团)有限公司 Geological disaster monitoring system and geological disaster monitoring terminal as well as surveillance and control center
CN103871203A (en) * 2012-12-13 2014-06-18 国家电网公司 Method and device for monitoring geological disaster
CN103093102B (en) * 2013-01-23 2015-12-02 中国科学院、水利部成都山地灾害与环境研究所 Based on the early stage dynamic prediction method of Debris Flow Evolution district disaster of earthquake and draught monitor
CN103197046B (en) * 2013-04-04 2015-04-01 中国科学院、水利部成都山地灾害与环境研究所 Method for measuring volume water content of debris flow critical soil body and debris flow pre-warning method
CN103278208B (en) * 2013-04-25 2015-07-15 丁勇 Inclined angle type scouring sensor and manufacture and installment method thereof
CN103489288A (en) * 2013-10-11 2014-01-01 中国地质调查局水文地质环境地质调查中心 Debris flow automatic motoring and early warning device and arrangement method thereof
CN103544810B (en) * 2013-10-15 2015-11-25 西南科技大学 A kind of rubble flow based on big-dipper satellite and GPRS is short faces method for early warning
CN103687385A (en) * 2013-12-12 2014-03-26 西安科技大学 Portable traffic natural disaster evaluation instrument
CN103994965B (en) * 2014-01-08 2016-04-13 北京师范大学 A kind of computing method of rainfall erosivity
CN104036650A (en) * 2014-03-05 2014-09-10 西安科技大学 Monitoring and early warning instrument for traffic natural disasters
CN104036153A (en) * 2014-07-02 2014-09-10 中国电建集团成都勘测设计研究院有限公司 Quantitative detection evaluation method for debris flow occurrence danger
CN104318103B (en) * 2014-10-23 2017-12-15 中国科学院、水利部成都山地灾害与环境研究所 A kind of landslide disaster monitoring and warning rainfall threshold determination method
GB2533396A (en) * 2014-12-19 2016-06-22 Senceive Ltd Monitoring system for railway embankment
CN104596459B (en) * 2015-01-21 2017-04-05 中核华泰建设有限公司 Landslide Forecast System and its monitoring method
CN104700577B (en) * 2015-03-16 2017-08-11 中国科学院、水利部成都山地灾害与环境研究所 A kind of precipitation method for early warning and its application in geological disaster
CN105239608A (en) * 2015-09-28 2016-01-13 中国地质大学(武汉) Landslide displacement prediction method based on wavelet transform-rough set-support vector regression (WT-RS-SVR) combination
CN105260833A (en) * 2015-10-14 2016-01-20 成都信息工程大学 Statistic analysis tool for preventing heavy rainfall disaster
CN105678965A (en) * 2016-01-28 2016-06-15 成都万江港利科技股份有限公司 Mountain torrent prediction device based on rainfall radar
CN106023530B (en) * 2016-06-07 2018-10-02 长安大学 A kind of heavy rain type diluted debris flow monitoring and prediction method for early warning
CN106023531A (en) * 2016-06-12 2016-10-12 吉林大学 Landslide early warning device based on monitoring multiple indexes
CN106157541B (en) * 2016-07-19 2018-08-21 成都理工大学 A kind of Debris Flow method for early warning and its application
CN105976569A (en) * 2016-07-26 2016-09-28 长安大学 Landslide hazard monitoring system and method
CN108205874A (en) * 2016-12-16 2018-06-26 航天科工惯性技术有限公司 Geo-hazard early-warning method based on multi-parameter linkage, live master station and system
CN106710148A (en) * 2016-12-28 2017-05-24 中国科学院深圳先进技术研究院 Geological disaster prediction method and system
CN106888249A (en) * 2016-12-30 2017-06-23 西安天和防务技术股份有限公司 The method for laying the sensory package for land resources monitoring
CN107067656A (en) * 2017-01-23 2017-08-18 重庆三峡学院 A kind of geological disaster monitoring system
CN106971510A (en) * 2017-03-30 2017-07-21 河海大学 A kind of double mode mountain torrents pre-alarming control system and method
CN109389808A (en) * 2017-08-06 2019-02-26 中铁二院工程集团有限责任公司 Lin-gang new city method based on creepage type landslide constant speed rate of deformation
CN107610421A (en) * 2017-09-19 2018-01-19 合肥英泽信息科技有限公司 A kind of geo-hazard early-warning analysis system and method
CN108008115A (en) * 2017-12-01 2018-05-08 白钰 Cave dwelling landslide disaster early warning deliverance apparatus and method under collapsible loess geological conditions
CN108593012A (en) * 2018-07-19 2018-09-28 中国地质调查局水文地质环境地质调查中心 The fast slowdown monitoring system of geological disaster
CN109920213B (en) * 2019-03-13 2020-10-23 河海大学 Method for carrying out real-time mountain torrent early warning based on critical rainfall of rainfall time-course distribution
CN109883480B (en) * 2019-03-15 2021-03-23 贵州理工学院 Method and system for advanced prediction of collapse of soil body in aeration zone
CN109949549A (en) * 2019-04-18 2019-06-28 甘肃省地质环境监测院 A kind of Geological Disaster Warning System
CN110415488B (en) * 2019-05-20 2021-07-13 宁波市水利水电规划设计研究院有限公司 Method, system, electronic equipment and storage medium for mountain torrent disaster early warning
CN110443980B (en) * 2019-07-22 2020-12-01 广州海洋地质调查局 Geological disaster monitoring device based on Internet of things
CN110568440B (en) * 2019-09-10 2020-09-15 四川省地质工程勘察院集团有限公司 Method for monitoring deformation of complex mountain area based on DS-InSAR technology
CN110631634B (en) * 2019-09-17 2020-09-18 同恩(上海)工程技术有限公司 Structure monitoring and evaluating method and system for complete inverse analysis and storage medium
CN110703359B (en) * 2019-10-16 2021-08-24 西北大学 Hysteresis nonlinear time forecasting method for landslide forecasting by taking rainfall as main part
CN110703360A (en) * 2019-10-16 2020-01-17 西北大学 Three-dimensional effect model for landslide prediction based on rainfall intensity and threshold value
CN110827514A (en) * 2019-11-12 2020-02-21 广州风雨雷科技有限公司 Geological monitoring system with geological disaster monitoring device
CN110988866A (en) * 2019-11-22 2020-04-10 上海熹翼科技有限公司 Multi-parameter debris flow early warning device based on radar
CN110880229A (en) * 2019-12-02 2020-03-13 北京深测科技有限公司 Geological disaster monitoring and early warning method and system
CN110940382A (en) * 2019-12-11 2020-03-31 重庆三峡学院 Geological disaster monitor
CN111931345B (en) * 2020-07-09 2021-11-02 西南交通大学 Monitoring data prediction method, device, equipment and readable storage medium
CN112071028A (en) * 2020-09-18 2020-12-11 北京中地华安地质勘查有限公司 Monitoring and early warning method and device for shallow landslide
CN111928784B (en) * 2020-09-27 2021-01-26 华思(广州)测控科技有限公司 Slope displacement monitoring device
CN112201010A (en) * 2020-10-09 2021-01-08 沈阳圣狩软件科技有限公司 Monitoring and early warning system and method for landslide
CN113255920B (en) * 2021-06-29 2021-09-28 中国科学院自动化研究所 Big data based dynamic system disaster cause and effect inference method and system
CN113311460B (en) * 2021-07-28 2021-10-26 湖南联智科技股份有限公司 Beidou-based early warning method and system

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0936589B1 (en) * 1998-02-17 2005-05-25 Mitsui Bussan Plant & Project Corp. Geographical displacement sensing unit
US7187277B2 (en) * 2003-05-28 2007-03-06 Nec Corporation Monitoring terminal device
CN101281678A (en) * 2008-05-23 2008-10-08 福州梦想现代科技有限公司 Automatic monitoring warning system for drawing triggering type landslide and rolling stone
CN101393269A (en) * 2008-11-06 2009-03-25 复旦大学 Method for monitoring geology by utilizing communication optical cable

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0936589B1 (en) * 1998-02-17 2005-05-25 Mitsui Bussan Plant & Project Corp. Geographical displacement sensing unit
US7187277B2 (en) * 2003-05-28 2007-03-06 Nec Corporation Monitoring terminal device
CN101281678A (en) * 2008-05-23 2008-10-08 福州梦想现代科技有限公司 Automatic monitoring warning system for drawing triggering type landslide and rolling stone
CN101393269A (en) * 2008-11-06 2009-03-25 复旦大学 Method for monitoring geology by utilizing communication optical cable

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张文君.滑坡灾害遥感动态特征监测及其预测分析研究.《中国优秀博士学位论文全文数据库》.2007, *

Also Published As

Publication number Publication date
CN102013150A (en) 2011-04-13

Similar Documents

Publication Publication Date Title
CN102013150B (en) System for predicting geologic hazard based on rainfall intensity, moisture content of slope soil and deformation
CN101477207B (en) Intelligent geological calamity synthetic monitoring system and multi-stage prediction analysis method
CN105096532B (en) A kind of geological hazards prediction system
Intrieri et al. Design and implementation of a landslide early warning system
CN106023530B (en) A kind of heavy rain type diluted debris flow monitoring and prediction method for early warning
CN101477206B (en) Geological calamity emergency monitoring, predicting and analyzing method
CN107817744A (en) Communal facility safety monitoring system
CN103743441A (en) Multi-factor coupling on-line monitoring system and multi-factor coupling on-line monitoring system method for slope safety
CN101826247A (en) System for monitoring, forecasting and warning mud-rock flow
Eberhardt et al. Improving the interpretation of slope monitoring and early warning data through better understanding of complex deep-seated landslide failure mechanisms
KR101788104B1 (en) System and method for real-time early warning for extreme rainfall-induced landslides
CN104655101A (en) High-precision lead type whole-section slope stability monitoring pre-alarming system and monitoring method thereof
KR102026821B1 (en) Remote measurement and management system for slope
Wang et al. Panzhihua airport landslide (Oct. 3rd 2009) and an emergency monitoring and warning system based on the internet of things
KR102124546B1 (en) Management and warning system of falling rock and soil and stone measure facilities to be communicated with the slope warning device
KR101845475B1 (en) Slope safety management system
Zhang et al. Real time remote monitoring and pre-warning system for Highway landslide in mountain area
CN106971510A (en) A kind of double mode mountain torrents pre-alarming control system and method
Azzam et al. Monitoring of landslides and infrastructures with wireless sensor networks in an earthquake environment
CN211740385U (en) Dangerous rock body collapse real-time early warning system based on multiple monitoring indexes
KR102042616B1 (en) IoT Based Safety Management System of Urban Construction Site
Kapoor et al. Real time monitoring & alert system for landslide
Xiao et al. Application and analysis on geological hazard monitoring and early warning system based on Internet of Things
Read et al. Frank Slide a century later: the Turtle Mountain monitoring project
Arbanas et al. Remote monitoring of a landslide using an integration of GPS, TPS and conventional geotechnical monitoring methods

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant