CN109000158A - Pipe leakage early warning system, method and its application based on Internet of Things and BIM - Google Patents
Pipe leakage early warning system, method and its application based on Internet of Things and BIM Download PDFInfo
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- CN109000158A CN109000158A CN201811025579.1A CN201811025579A CN109000158A CN 109000158 A CN109000158 A CN 109000158A CN 201811025579 A CN201811025579 A CN 201811025579A CN 109000158 A CN109000158 A CN 109000158A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
Abstract
The present invention provides a kind of pipe leakage early warning system, method and its application based on Internet of Things and BIM, is made of BIM Visualization Model, signal pickup assembly, data storage and analysis system etc..Firstly, establishing BIM model, all kinds of entity attribute information such as pipeline, surrounding soil, roadbed are assigned in a model, are deduced by analysis software emulation, are obtained the safe design value of each parameter；Real-time monitoring is carried out to each entity by sensor again, if monitor value is more than that safe design value will trigger pre-warning signal；It is got rid of the danger on the spot finally by precise positioning.The invention has the following advantages: warning information can timely and effectively be issued, and the potential disaster point of precise positioning, the real-time monitoring and early warning of pipeline are realized.
Description
Technical field
The present invention relates to a kind of pipe leakage early warning system, method and its application based on Internet of Things and BIM.
Background technique
Phenomena such as generally existing fracture of outmoded municipal water-feeding drainaging line, disconnection, mismatch, leakage, the water of pipeline leakage mouth
Stream, which is invaded, washes away erosion surrounding soil, forms eroded crater in addition flood season heavy rainfall causes Near Pipelines to settle and causes Near Pipelines
Soil erosion further speeds up the formation of Collapse Pit, and the final road surface that induces collapses accident.
Internet of Things is to obtain object using technologies such as radio frequency identification, wireless sensor network, global positioning systems
The multidate information of body is then blazed abroad multidate information by network, finally utilizes the intelligence computations such as cloud computing, fuzzy diagnosis
Technology handles data information, to implement intelligentized control method to object, have can perceive, can interconnect, the spies such as intelligence
Point.BIM is the basic data model of Internet of Things application, it is possible to provide virtual reality model integrates various projects by parameter model
Possessed real information, and information synergism, shared and transmitting are provided during Whole Life Cycle of Projects, have it is visual,
The features such as analog.
BIM (Building Information Model) technology is during civil engineering construction to convert traditional planar design to
Three Dimensional Design Model can carry out visual Simulation, lifting construction efficiency and scientific control to work progress, give construction enterprises' band
It is greatly worth, those skilled in the art usually use pipeline modeling software BIM-Revit software to the existing pipe in city at present
Line realizes modeling, in addition, applying BIM and Internet of Things multidimensional integration technology in pipeline monitoring, shows pipe using three dimensional form
Road information and reach data it is real-time acquisition and update have become inexorable trend；Chinese patent application: application number:
CN201510957264 discloses a kind of pipe network real-time monitoring system and its working method, pipe network real-time monitoring system, feature
It is that it includes RFID electronic label, Internet of Things sensing module, wireless real-time data transmission module and monitoring center, the RFID
Electronic tag is pasted onto along pipeline and on node and Internet of Things sensing module, and the Internet of Things sensing module input terminal acquires number
According to, the input terminal of the output end connection real-time Data Transmission module of Internet of Things sensing module, the real-time Data Transmission module
Output end connects the input terminal of monitoring center, and the monitoring center carries out data analysis, and the Internet of Things sensing module is assets
Management module, attitude monitoring module, valve pit monitoring modular, corrosion monitoring module, in register one's residence monitoring modular or pinpoint module
At least one；The working method of the pipe network real-time monitoring system, it is characterised in that specific step is as follows: 1) Internet of Things senses
Modules acquiring data；(2) Internet of Things sensing module gives collected data to prison by wireless real-time data transmission module transfer
Control center；(3) data received are carried out data statistics by monitoring center and data are analyzed.
However, the above-mentioned prior art do not pass through BIM Visualization Model real-time monitoring pipeline leakage, while also not by
The surface collapse caused by pipe leakage realizes early warning and realizes that precise positioning is got rid of the danger on the spot.
Summary of the invention
In order to solve the deficiency in the prior art, the present invention is intended to provide a kind of pipe leakage based on Internet of Things and BIM
Early warning system, method and its application, while realizing identification and prevention before pipe leakage, road surface collapse, thus for pipe leakage,
Road surface collapses accident emergency management etc. and provides the technology platform of science decision.
Its technical solution is as follows:
Pipe leakage early warning system based on Internet of Things and BIM, including BIM Visualization Model, signal pickup assembly, ground
Server, it is characterised in that: information collecting device is the sensor acquisition pipeline data being arranged on distributed pipeline, and by institute
It states data and is uploaded in ground-based server and analyzed, calculated, stored, and the real-time display scene feelings in BIM Visualization Model
Condition；Meanwhile server judges pipe leakage position and provides road surface to collapse early warning according to analysis data.
Invention additionally discloses a kind of method for early warning of pipe leakage based on Internet of Things and BIM, it is characterized in that:
Step 1: establishing BIM model；
Step 2: being deduced, obtained as model platform analysis, emulation by NavisWorks Manage2015 software module
The safe design value of each parameter；
Step 3: acquisition distributed pipes track data；
Step 4: acquisition data being uploaded respectively and carries out constantly data in BIM model and shows, while will be in the data of acquisition
Ground-based server is passed to be handled, stored.
Step 5: being got rid of the danger on the spot by precise positioning.
It is preferred that are as follows: the data of acquisition are compared the ground-based server with design value, if it exceeds design safety value
Then trigger alarm mechanism；
It is preferred that are as follows: BIM model anticipation road surface collapses to form the time, while amendment design value.
The pipe leakage early warning system based on Internet of Things and BIM that the invention also discloses a kind of applied to city road network,
Method.
The invention has the following advantages that
One) can timely and effectively issue warning information, and precise positioning pipe leakage point, realize risk identification, perception with
Early warning；
Two) monitoring data real-time display in Building Information Model BIM system, intuitive, visuality are strong.
Detailed description of the invention
Fig. 1 is present system structural schematic diagram.
Fig. 2 is probabilistic neural network structural schematic diagram in inventive pipeline leakage orienting.
Specific embodiment
The present invention will be further described in detail with reference to the accompanying drawings and detailed description.
Pipe leakage early warning system based on Internet of Things and BIM, including BIM Visualization Model, signal pickup assembly, ground
Server, the information collecting device are the sensor acquisition pipeline data being arranged on distributed pipeline, and by the data
It is uploaded in ground-based server and is analyzed, calculated, stored, and the real-time display field condition in BIM Visualization Model；Together
When, server judges pipe leakage position and provides road surface to collapse early warning according to analysis data.
The present invention discloses a kind of pipe leakage method for early warning based on Internet of Things and BIM, includes the following steps:
Step 1: establishing BIM model；
Step 2: being deduced by NavisWorks Manage2015 software as model platform analysis, emulation, obtain each ginseng
Several safe design values；
Step 3: acquisition distributed pipes track data；
Step 4: acquisition data being uploaded respectively and carries out constantly data in BIM model and shows, while will be in the data of acquisition
Ground-based server is passed to be analyzed, handled, stored.
Step 5: being got rid of the danger on the spot by precise positioning.
The foundation of grid BIM model
The present invention is modeled using prior art Autodesk Revit software realization, software integration Autodesk
The function of Revit Architecture, Autodesk Revit MEP and Autodesk Revit Structure three classes software
Can, it aims at Building Information Model (BIM) and constructs, be widely applied in the modeling of civil buildings at home.Building Information Model
(Building Information Modeling, abbreviation BIM) be every relevant information data of construction-engineering project as
Corresponding buildings model is built on the basis of model, using real information possessed by digital information analogue simulation building, is being built
Build or the planning and designing of engineering project, construction, operation management to the Life cycle for scrapping dismounting production and management in
Important function is played.
Revit modeling pattern of the present invention is to model DWG file importing Revit software, and specific modeling procedure is such as
Under:
1, grassroot project
The grassroot project in Revit software, title.
2, absolute altitude is arranged
" common " → " benchmark " → " absolute altitude " is selected to carry out the drafting of absolute altitude, absolute altitude is numerous pels in Revit modeling
Positioning datum, specific absolute altitude algorithm are as follows: setting the mark point outside pipeline are as follows: P (pointX, pointY), two terminal As of pipeline
(x1, y1), B (x2, y2), intersection point coordinate are N (x, y), then vector
ByWithIt can vertically obtain
That is: (x-pointX) (x2-x1)+(y-pointY) (y2-y1)=0,
Pipeline position has following several situations:
(1) parallel pipeline is parallel with X-axis, and intersection point coordinate is N (pointX, y1).
(2) parallel pipeline is vertical with X-axis, and intersection point coordinate is N (x1, pointY).
(3) parallel pipeline is neither parallel nor vertical with X-axis with X-axis, intersection point coordinate:
N (k (x2-x1)+x1, k (y2-y1)+y1), wherein slope k:
It can be obtained by above formula:
To sum up algorithm, absolute altitude algorithm description of the present invention are as follows: input: mark point；Output: it the automatic absolute altitude of pipeline batch: obtains
Take two mark points on parallel pipeline group both sides；The pipeline for obtaining one group of straight line intersection constituted with the two mark points is believed
Breath；It sorts to this group of pipeline；Any mark point is found with the method for Perpendicular Line Theorem and arrives the intersection point of pipeline, and makes vertical line；It is logical
Intersection point is crossed to make lead and carry out absolute altitude one by one to pipeline.
3, axis net is arranged
" common " → " benchmark " → " axis net " is selected to carry out the drafting of axis net, axis net is benchmark and the key institute of model creation
For positioning column, wall etc..
4, CAD to Revit is linked
The three-dimensional visualization for realizing pipe gallery needs for completed pipe gallery CAD diagram to be loaded into Revit and make
3 D rendering is carried out for base map.Revit loads the mode of CAD diagram using " link CAD " function；" link CAD " function makes CAD
File and Revit file keep linking relationship, i.e., when linked cad file is changed, can real time reaction to Revit
In file.
5, the professional family library race for establishing piping lane is the core of Revit software, and all pels are on the basis of race
Upper foundation, all kinds of races such as building, structure, electromechanics, fire-fighting are covered in library in Revit race.But since pipe gallery covers a plurality of pipeline,
Simultaneously comprising a plurality of types of equipment such as sewage pipeline, tap water pipeline, shutoff valve, pressure instrumentation, these pipelines with set
Standby specifications parameter is different, substantial amounts, and Revit race library is not covered by pipe gallery whole specification component.Therefore this hair
It is bright by family of clouds 360 supplement pipe gallery associated components, 360 platform of family of clouds not only provide pipe gallery professional family with it is open
Supplier products library, and the component in race library be continuously updated with improve in, this greatly facilitates user and is modeling
It constantly updates in the process and improves pipe gallery race library.
6, the three-dimensional visualization of pipe gallery
After race library supplement is improved, first on the basis of absolute altitude and axis net, race library is called to complete building, structure, electrical, warm
Logical, each profession of pipe gallery affiliated facility modeling；Secondly, the model of each profession is integrated, realize that pipe gallery is complete
The foundation of Professional Model.During Visualization Modeling, all kinds of pipelines, equipment, the correlation attribute information of component all include
In threedimensional model, this provides accurate complete database for later period piping lane construction maintenance.
7, collision detection
After the completion of piping lane threedimensional model is tentatively established, the BIM model created using the collision checking function scanning of Revit,
Quickly, conflict pel is accurately and efficiently found out, and collision is submitted to report, referring to the modification adjustment of report carry out scheme.
It directly can directly be checked using the MEP modeling software of BIM in the collision detection problem of pipeline.It is utilizing
BIM can obtain accurate conflict point position after carrying out collision detection to model and generate conflict report, then straight using software
It connects and modifies and adjust to conflict point.The inspection why BIM technology can provide collision for us, which services its reason, three
Point, first, software is monitored by unit object of pel；Second, digitization decomposition is carried out to monitoring unit using mathematics；The
Three, associated pel function is formed into simultaneous equations and is solved, to obtain between monitoring object with the presence or absence of problem.
Check that collision problem is mainly to determine that whether there is for design touches by the inspection between pel using BIM software
Hit problem.
Checking collision problem using Revit, steps are as follows:
1, the collision detection function under cooperation catalogue is clicked in functional areas；
2, selection participates in pel classification and the source of collision；
3, it clicks determination key and carries out collision detection.
Through the above steps, the BIM visual modeling for the pipeline of being detected is completed.
Pipe leakage state down-off changing rule
Certain point leaks in grid, that is, has the relative pressure of the point in pressure pipe section to be reduced to zero suddenly, necessarily cause
Water flow in pipeline section in communication follows law of conservation of energy and the law of conservation of momentum, and " preferential " flow direction is seeped under pressure
Leak source.Each fluid particle stress changes in pipeline under leakage states.After leakage occurs, due to the opposite pressure at downstream leakage
Power is down to zero, and each particle in breakthrough upstream is under pressure P effect with acceleration a departing from original motion profile and along new rail
Mark migration, vector acceleration a ' are positive in the component for being directed toward breakthrough direction, that is, the flow for flowing to breakthrough constantly increases.Foundation
Conservation of mass theorem leaks pipe network for generation, seepage discharge is concentrated on according to certain rules at the node of pipe network, or
It sees breakthrough as a new node, then has flux balance equations:
Q0+Qz=QL+Q1 (1)
Q0 in formula --- pipeline section inlet flow rate, m3/s；
Qz --- the flow increment of breakthrough upstream pipeline section, m3/s caused by leaking；
QL --- seepage discharge, m3/s；
Q1 --- breakthrough downstream pipeline flow, m3/s, and 0 < Qz < QL, are denoted as Qz=aQL, 0 < a < 1.
Therefore according to above-mentioned pipe leakage rule, we make following analytical calculation to pressure current pipeline, gravity stream pipeline:
Pressure current pipeline
The permission leakage of pressure current pipeline is calculated according to material by formula (1)~(4).
Steel pipe:
Cast iron pipe:
Seif-citing rate, prestressed concrete pipe:
Plastic tube: qF=0.00028PTD (4)
QF in formula --- test allows leakage, L/ (minkm)；
D --- internal diameter of the pipeline, mm；
PT --- test pressure, MPa.
For pressure pipeline, since operating pressure is different from test pressure, pressure correction should be carried out, pipe leakage can be seen
Make small orifice outflow, outflow is directly proportional to 1/2 power of pressure, then leakage can be calculated by formula (5) under operating pressure:
Q in formula --- operating condition leakage, L/ (minkm)；
P --- operating pressure, MPa.
Gravity stream pipeline
Gravity stream pipeline allows leakage to be calculated according to material by formula (6) and formula (7).
Cast iron pipe, plastic tube: qG=0.0032D (6)
Pipe of concrete:
Q in formulaG- test allows leakage, L/ (minkm).
Pipe full water is made head be higher by tube top 2m by gravity stream pipeline, and pipeline pressure-bearing is simultaneously in flowing full state, therefore
Leakage should carry out pressure correction first, and method is the same as pressure current pipeline.Further, since gravity stream pipeline generallys use non-flowing full fortune
The Area of Wetted Surface of row, tube wall is less than flowing full pipeline, if pipe leakage amount is directly proportional to immersion tube wall area, also to pipe leakage amount
It should carry out non-flowing full amendment.
After pressure correction and the amendment of non-flowing full, operating condition leakage is calculated by formula (8):
α-pipe full degree in formula.
Therefore the distributed sensor being located on pressure current pipeline and gravity stream pipeline will test data and upload to demodulation
The detection data by demodulator feedback real time demonstration into BIM Visualization Model, while being uploaded to ground-based server by device
In analyzed, calculated, store to obtain pipe leakage information in underground piping.
The positioning of pipe leakage point
The present invention uses the accurate positionin of probabilistic neural network model realization pipe leakage point.
Probabilistic neural network model is the artificial neural network established based on probability theory thought.From practical experience, I
The diagnosis of the leakage loss of water supply line is built upon experienced, judged further according to real time data.This meets pattra leaves just
For the optimization decision rule of minimum expectation risk in this classifying rules.For Bayes classification rule, probability answer to a riddle position,
But it can be made an estimate by a large amount of measured data, it can neural network is trained using these empirical Frequencies, thus
Estimate the conditional probability in Bayesian Estimation: p (X | f k):
In above formula, X is the sample vector for needing probabilistic neural network to identify, W is mode sample vector, and m is vector dimension,
σ is known as smoothing factor, and XT is sample vector transposed matrix.Specifically, using above method design conditions probability, actually
Centered on each network node, the sum of each Gaussian node function is calculated, and smoothing factor σ is exactly sample Gaussian letter just
The corresponding standard deviation of number.The model is divided into four layers, i.e. input layer, mode layer, summation layer and decision-making level.The model it is main
Feature is that neuron set by mode layer is quantitatively identical with the sum of recognition training sample, sample value and power in mode layer
The calculation method of coefficient is identical with conventional manual's neural computing mode, i.e., the nodal value in mode layer is with vector multiplications
Mode is expressed.In summation layer, the connection type between each unit be it is specific, i.e., only and correspond the mode list of classification
Member sets up connection, and method for calculating probability of all categories is Parzen calculation method above.And in decision-making level, then
It is that maximum a posteriori probability is determined that input vector corresponds to after posterior probability is sorted by size according to Bayes classification rule
Not.
Water supply line leakage loss is positioned using probabilistic neural network model, can be carried out by following basic step, point
Input is not provided for each layer of probabilistic neural network.1. extracting water supply line operation characteristic parameter as the defeated of probabilistic neural network
Enter, in order to more fully the considerations of, it is proposed that carry out the data of approximate substitution missing with the following method: with the two neighboring moment
Monitoring pressure value input vector of the difference as probabilistic neural network.It can use sensor RTU sampling frequency with this method
The high feature degree of approximation of rate is preferable.2. determining that the basic thought of probabilistic neural network structure probability neural network method is exactly benefit
With a large amount of water supply line fault sample, fault mode, therefore the structure in order to determine network are therefrom analyzed, failure need to be extracted
The sample of feature.A certain number of fault samples should be only reserved to be used as check.Network structure design on, using with
RTU quantity equal number of nodes determines input layer number, and the number of nodes in mode layer then needs and the failure that provides
Sample size is identical, and the number of nodes in decision-making level is determined according to the fault type grasped, how many kind can
How many decision node layer the fault type of energy just corresponds to, it should be pointed out that it is possible that reporting by mistake in sensor, therefore certainly
A normal condition node is reserved in plan layer from the point of view of probability.Probabilistic neural network model schematic is as shown in Figure 2.
The model is neural network model based on probability, therefore exporting result is still probability value, i.e., with different probability
Form provide a possibility that sample belongs to certain class failure, wherein the maximum i.e. be judged as that the sample belongs to such failure.This
Invention is extracted 70 groups of fault signature samples for training neural network, and 6 groups of samples is separately taken to be used to check the training knot of the network
Fruit.The water pressure at each node is taken to change RTU monitor value in network input layer, mode layer corresponds to fault sample quantity, i.e., total
Take 70 nodes.According to known fault type 23 in decision-making level, wherein including a normal condition, i.e. section in decision-making level
Point quantity is 24.
According to above-mentioned network model, on the basis of being trained based on to 70 groups of training samples, to 6 groups of check samples
It is checked.Calculated result is as follows:
As seen from the above table, in 3 groups of check samples involved in 6# fault mode, it is under the jurisdiction of the fault mode with the 2nd group
Maximum probability, the probability for belonging to the fault mode in other two groups of samples in comparison are lower.Therefore, it is considered that the 2nd group of sample belongs to
6# fault type.The present invention qualitatively judges pipe network with the presence or absence of leakage loss using probabilistic neural network model, is distinguishing out
Existing instrument and equipment and hydraulic pipeline model is cooperated more can accurately to position generation on the basis of existing Leakage Pipes fault type
The position of leakage loss.
In order to realize the continuous real-time perception of more physical quantitys of geological conditions, acquisition and the transmission energy of security information are improved
Technology of Internet of things is introduced into this early warning system by power, has multiple physical field coupled inferring, high-precision, automatic using sensor
Continuously, electromagnetism interference, do not influenced by water and wet environment and its survival rate is high, highly reliable and can transmit at a distance etc. excellent
Sensor is arranged in point at the key node of the entities such as existing underground utilities, surrounding soil, roadbed, guarantees sensor and each
Physical couplings are good, are monitored to the temperature of all kinds of entities, strain, surrounding soil seepage flow situation etc..When data acquire, by counting
Acquisition is scanned to sensor according to collector, the related data informations such as temperature, flow is obtained, data is uploaded to ground service
Device carries out operational analysis, the safety-relevant datas such as underground utilities and its ambient enviroment is calculated, and in the BIM model built
Real-time display.The related data that actual monitoring obtains is compared with the safe design value that BIM model is arranged, if being more than safety
Design value will trigger the historical data in invoking server and carry out Gernral Check-up and security evaluation, and integrate relevant information, real
Existing intellectual analysis is studied and judged, at the same will in analysis result deposit database, and by BIM system carry out multidimensional show it is pre- with risk
It is alert.According to being accurately positioned, to potential empty disaster point, the row such as is checked, and takes digging up and filling in, slip casting as needed in scene one by one
Dangerous measure.
Pipe leakage early warning system based on Internet of Things and BIM includes BIM Visualization Model, signal pickup assembly, ground
Server, information collecting device are the sensor acquisition pipeline data being arranged on distributed pipeline, such as flow, stress letter
Breath, and the data are uploaded in ground-based server and analyzed, calculated, storage obtains underground pipeline information,
NavisWorks Manage2015 (visualization and simulation management software) system as in model demonstration platform, establishes BIM mould
Type assigns in pipeline all kinds of entity attribute information such as flow parameter, surrounding soil, roadbed and on influencing under roadbed in a model
Cohesive strength and internal friction angle, the across footpath of underground cavity, the factors such as overburden layer thickness on underground cavity for the soil that underground cavity collapses
Give referential data, carry out emulation deduction, compare all data, obtain pipe leakage and pipeline surface collapse formed because
The safe design value of element, and compared with the acquisition data realization of scene feedback, if monitor value is more than that safe design value will trigger
Pre-warning signal, in addition it is also possible to be dynamically modified to the safe design value according to actual monitoring result.
It is to be washed away because of subgrade soils by water, dive and lose that road surface, which collapses, caused by grogs outflows with water.The raw quicksand of local products or erosion of diving
Main cause has classification, compaction rate, grain composition, water penetration and the dynamic water etc. of soil.Under the action of dynamic waterpower, gradation is not
The fine sand or silt good, the soil is porous is easy to happen quicksand or latent erosion phenomenon.Hair when place's pipe excavation is repaired is collapsed in road pavement
Existing, the pipeline for collapsing serious location is vertically badly deformed, and is crushed substantially, and liquidization and flow state is presented in the pipeline soil body, on
The a large amount of soil of siltation, about 2/3 duct height of deposit depth in the pipeline of trip.Intuitively see that the immediate cause that road surface collapses is exactly to manage
Road leak, the pipeline soil body be lost after, the unbearable top overburden load of pipeline and collapse.
In engineer application, typing underground utilities, surrounding soil, road surface, roadbed, periphery are built in BIM Visualization Model
Equal entities are built, assign all kinds of entity attribute information in a model, and be associated with these information, building entity letter related to function
Breath is to provide virtual reality model；Pass through NavisWorks Manage2015 (visualization and simulation management software) system conduct
Model platform, at the same be based on BIM model, and under roadbed influence underground cavity collapse soil cohesive strength and internal friction angle,
The calculating of the factors binding isotherm such as overburden layer thickness gives referential data (such as underground cavity in the across footpath in lower cavity, underground cavity
Across footpath is that theoretical reference value is 0.5 meter, then referential data is 1.2-1.5 times of theoretical value, then 0.6 meter of selection etc., above-mentioned specific
Calculate and the conversion relation between reference value and theoretical value please refer to this field databook), emulation deduction is carried out, calculate,
Every feedback data is compared, the safe design value for collapsing formative factor is obtained, provides reference for actual monitoring, and according to practical prison
Result is surveyed dynamically to be modified the safe design value.
Early warning system, the method for pipe leakage based on Internet of Things and BIM in the present invention can be widely applied to city pipe
Net collapses realization real-time monitoring to leak caused road surface to urban duct.
Many details are elaborated in the above description to fully understand the present invention.But above description is only
Presently preferred embodiments of the present invention, the invention can be embodied in many other ways as described herein, therefore this
Invention is not limited by specific implementation disclosed above.Any those skilled in the art are not departing from the technology of the present invention simultaneously
In the case of aspects, all technical solution of the present invention is made using the methods and technical content of the disclosure above many possible
Changes and modifications or equivalent example modified to equivalent change.Anything that does not depart from the technical scheme of the invention, according to this
The technical spirit of invention any simple modifications, equivalents, and modifications made to the above embodiment, still fall within skill of the present invention
In the range of the protection of art scheme.
Claims (7)
1. a kind of pipe leakage early warning system based on Internet of Things and BIM, including BIM Visualization Model, signal pickup assembly,
Face server, it is characterised in that: information collecting device is the sensor acquisition pipeline data being arranged on distributed pipeline, and will
The data, which are uploaded in ground-based server, to be analyzed, is calculated, being stored, and the real-time display scene in BIM Visualization Model
Situation；Meanwhile server judges pipe leakage position and provides road surface to collapse early warning according to analysis data.
2. the pipe leakage early warning system according to claim 1 based on Internet of Things and BIM, it is characterized in that: the BIM can
Underground utilities, surrounding soil, road surface, roadbed, neighboring buildings attribute information are assigned in model depending on changing, and there is visual and analog
Property.
3. the pipe leakage early warning system according to claim 1 based on Internet of Things and BIM, it is characterized in that: the sensing
Device is arranged in underground piping, surrounding soil, at roadbed node.
4. a kind of method based on any pipe leakage early warning system based on Internet of Things and BIM of claim 1-3, special
Sign are as follows:
Step 1: establishing BIM model；
Step 2: being deduced by NavisWorksManage2015 software as model platform analysis, emulation, obtain each parameter
Safe design value；
Step 3: acquisition distributed pipes track data；
Step 4: data will be acquired by demodulator upload respectively and carry out constantly data in BIM model and show, while by acquisition
Data upload to ground-based server and are analyzed, handled, stored；
Step 5: being got rid of the danger on the spot by precise positioning.
5. the method according to claim 4, it is characterized in that: the ground-based server carries out the data of acquisition and design value
It compares, if it exceeds design safety value then triggers alarm mechanism.
6. a kind of applied to the pre- according to any pipe leakage based on Internet of Things and BIM of claim 4-5 of urban pipe network
The method of alert system, it is characterized in that: the application is for monitoring since pipe leakage causes road surface to collapse.
7. a kind of -3 any pipe leakages based on Internet of Things and BIM according to claim 1 applied to urban pipe network are pre-
Alert system, it is characterized in that: the application is for monitoring since pipe leakage causes road surface to collapse.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109920318A (en) * | 2019-04-22 | 2019-06-21 | 华侨大学 | A kind of simulator of roadbed Void Model |
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| CN109797977A (en) * | 2019-03-22 | 2019-05-24 | 浙江广厦建设职业技术学院 | A kind of cement mortar masonry building system and its working method based on BIM buildings model |
| CN109920318A (en) * | 2019-04-22 | 2019-06-21 | 华侨大学 | A kind of simulator of roadbed Void Model |
| CN109920318B (en) * | 2019-04-22 | 2024-02-02 | 华侨大学 | Simulator of roadbed cavity model |
| CN110107819A (en) * | 2019-05-17 | 2019-08-09 | 河南工业大学 | A kind of petroleum chemicals conveyance conduit leakage monitoring early warning system and method |
| CN110309620B (en) * | 2019-07-10 | 2023-08-11 | 河北省建筑科学研究院有限公司 | Underground pipe gallery fire burst monitoring system based on Internet of things and BIM and implementation method |
| CN110309620A (en) * | 2019-07-10 | 2019-10-08 | 河北省建筑科学研究院有限公司 | Based on the underground pipe gallery fire of Internet of Things and BIM explosion monitoring system and implementation method |
| CN111027116A (en) * | 2019-10-31 | 2020-04-17 | 万翼科技有限公司 | Building operation and maintenance management method and device, computer equipment and storage medium |
| CN111383423A (en) * | 2020-02-13 | 2020-07-07 | 江苏大学 | Road collapse early warning method and system based on drainage pipe network flow monitoring |
| CN111383423B (en) * | 2020-02-13 | 2021-11-23 | 江苏大学 | Road collapse early warning method and system based on drainage pipe network flow monitoring |
| CN111828846A (en) * | 2020-07-02 | 2020-10-27 | 广州新利堡消防工程企业有限公司 | Fire-fighting engineering pipeline water leakage detection method, device, equipment and storage medium |
| CN112597615A (en) * | 2020-12-21 | 2021-04-02 | 常州市捷甲非开挖管道技术有限公司 | BIM-based sewer pipeline management and control method and system |
| CN113657019A (en) * | 2021-07-06 | 2021-11-16 | 大唐互联科技(武汉)有限公司 | Heat supply pipe network early warning system |
| CN113883423A (en) * | 2021-10-19 | 2022-01-04 | 山东腾威石油装备有限公司 | Novel pipe network repair reinforcing method |
| CN115218134A (en) * | 2022-07-08 | 2022-10-21 | 江苏天禧电力与照明景观工程技术有限公司 | Road monitoring panoramic information sensing method and system based on 5G Internet of things |
| CN115218134B (en) * | 2022-07-08 | 2023-11-21 | 江苏天禧电力与照明景观工程技术有限公司 | Road monitoring panoramic information sensing method and system based on 5G Internet of Things |
| CN115146513A (en) * | 2022-07-26 | 2022-10-04 | 北京科技大学 | Pipeline leakage collapse simulation early warning method and system |
| CN115294461A (en) * | 2022-10-10 | 2022-11-04 | 中国电建集团山东电力建设第一工程有限公司 | Power facility collapse assessment method and system based on BIM and remote sensing image |
| CN115294461B (en) * | 2022-10-10 | 2023-01-31 | 中国电建集团山东电力建设第一工程有限公司 | Power facility collapse and inclination assessment method and system based on BIM and remote sensing image |
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