CN109408686A - A kind of underground pipe gallery big data visualization system and method - Google Patents
A kind of underground pipe gallery big data visualization system and method Download PDFInfo
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Abstract
The invention belongs to pipe gallery field of engineering technology, disclosing a kind of underground pipe gallery big data visualization system and method, the underground pipe gallery big data visualization system includes: video monitoring module, environmental data detection module, central control module, cloud service module, device management module, risk evaluation module, alarm module, display module.The equipment of security protection, ventilation, power supply, draining, illumination and fire-fighting in piping lane and Cloud Server platform are established Internet of Things communication framework by management module by the present invention, and technology of Internet of things is utilized, greatly facilitates the management of O&M and equipment；Simultaneously, the present invention passes through risk evaluation module, pipe gallery disaster chain Bayesian network model can be constructed according to the Evolution of Potential hazards a variety of in pipe gallery, the probability and the assessment piping lane extent of damage that each disaster of piping lane occurs are predicted, to propose scientific and effective pipe gallery mitigating the disaster by cutting the disaster chain in the headstream mechanism and measure.
Description
Technical field
The invention belongs to pipe gallery field of engineering technology more particularly to a kind of underground pipe gallery big data visualization systems
System and method.
Background technique
Pipe gallery is exactly the comprehensive corridor of underground urban duct.I.e. Urban Underground build a tunnel space, by electric power,
Communication, the various utilities pipelines such as combustion gas, heat supply, plumbing are integrated in one, and are equipped with special access hole, hoisting port and monitoring system
System implements unified planning, Uniting, unified construction and management, is the important infrastructure and " life for ensureing city operations
Line ".However, existing pipe gallery equipment is numerous, management trouble；Meanwhile the operation experience accumulation of pipe gallery is less, to pipeline
Enter corridor analysis and the research of pipeline, Environmental coupling relationship is insufficient, is unable to comprehensive assessment pipe gallery risk, it is potential to result in it
There are uncertain, the features such as diversity and linksystem in disaster source.
In conclusion problem of the existing technology is: existing pipe gallery equipment is numerous, management trouble；Meanwhile it is comprehensive
The operation experience accumulation of piping lane is less, enters corridor analysis to pipeline and the research of pipeline, Environmental coupling relationship is insufficient, cannot integrate and comment
Estimate pipe gallery risk, results in its potential disaster source and there are uncertain, the features such as diversity and linksystem.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of underground pipe gallery big data visualization system and
Method.
The invention is realized in this way a kind of underground pipe gallery big data method for visualizing, the underground pipe gallery
Big data method for visualizing includes:
Step 1, video monitoring module monitor pipe gallery equipment working state using image pick-up device；Environmental data detects mould
Block utilizes temperature, humidity, depth of accumulated water, oxygen concentration, H in sensor detection corridor2S concentration and CH4Concentration data information；
Step 2, central control module concentrate big data resource to the number of detection by cloud service module using Cloud Server
According to being handled；Internet of Things integrated management piping lane relevant device is utilized by device management module；Pass through risk evaluation module pair
Piping lane disaster chain risk is assessed；
Step 3 alarms to piping lane disaster accident using alarm by alarm module；It is utilized by display module
The data and monitored picture of display display detection；
The device management module management method is as follows:
(1) Internet of Things framework is established, Internet of Things framework includes sensing layer, network layer and application layer, and application layer includes that cloud is flat
Platform and WEB page create the virtual three-dimensional model of piping lane in cloud platform；
Sensing layer includes equipment and operation maintenance personnel GPS positioning device in piping lane, and the equipment in the piping lane includes security protection
Equipment, ventilation equipment, power supply unit, drainage equipment, lighting apparatus and fire-fighting equipment；
Network layer includes the access network and communication network of Internet of Things, for security device, ventilation equipment, power supply unit,
Data access and communication between drainage equipment, lighting apparatus and fire-fighting equipment, and make security device, ventilation equipment, power supply
Data interaction is realized between equipment, drainage equipment, lighting apparatus, fire-fighting equipment and cloud platform；
(2) user account database is created in cloud platform, is used for administrative staff's essential information, and mention on the WEB page
For log-in interface；
(3) equipment management data library is created in cloud platform, its step are as follows:
Asset of equipments database is established, record capital budgeting in recent years goes out storage statistics, each system fund accounting, pipeline
Failure rate, maintenance project completion rate, auxiliary device quantity statistics, equipment fault statistics and equipment scrapping statistics；
Attribute information is established for all devices in piping lane, and creates index；The attribute information includes device numbering, sets
Standby type, device name, device model, parameter, measurement unit, support area and support time；
Equipment is provided in WEB page and adds interface, user adds necessary the believing of interface input newly added equipment by equipment
Breath, after user has inputted the necessary information of newly added equipment, cloud platform adds the number of newly-increased equipment in equipment management data library
According to；Necessary information includes device numbering, device type, device name, device model, parameter, measurement unit and support area；
(4) wisdom monitor database and wisdom are created and monitor WEB page, wisdom monitor database record fire-fighting system,
Ventilating system, power supply system, lighting system, the working condition of drainage system and security system；With the side of chart in WEB page
Formula shows the operating state data of fire-fighting system, ventilating system, power supply system, lighting system, drainage system or security system；
(5) inspection management database and inspection management WEB page, inspection management data-base recording Daily Round Check letter are created
Breath, workform management data and patrol officer's location information, and Daily Round Check is graphically shown in inspection management WEB page
Information, workform management data and patrol officer's location information；
(6) contingency management database and contingency management WEB page are created, all emergency of contingency management data-base recording are pre-
Case, contingency management WEB page show all emergency preplans；
(7) O&M knowledge data base is created, records the relevant knowledge and experience of piping lane O&M, and pass through WEB page presentation
The relevant knowledge and experience of piping lane O&M；In creation O&M report database, the O&M report in per January or year is recorded, and is passed through
WEB page shows the O&M report in per January or year.
Further, the risk evaluation module appraisal procedure is as follows:
Firstly, monitoring the risk assessment parameter that risk data determines pipe gallery, the O&M according to pipe gallery O&M
Monitoring risk data includes environmental monitoring data corresponding to the alert event or Disaster Event that occur during pipe gallery O&M
And the frequency occurred；
Secondly, constructing the Bayesian network model of pipe gallery disaster chain according to the risk assessment parameter；
Then, the risk assessment of pipe gallery disaster chain is carried out according to the Bayesian network model；
Finally, implementing corresponding mitigating the disaster by cutting the disaster chain in the headstream measure according to assessment result.
Further, described that the risk assessment parameter packet that risk data determines pipe gallery is monitored according to pipe gallery O&M
It includes:
The collection of risk data is carried out, the risk data includes the alert event occurred during pipe gallery O&M or calamity
Environmental monitoring data and the frequency of generation corresponding to evil event；
Flood inducing factors and corresponding hazard-affected body are selected according to the risk data of acquisition and factors causing disaster.
Further, the Bayesian network model packet that pipe gallery disaster chain is constructed according to the risk assessment parameter
It includes:
The coupled relation of each disaster is determined according to selected Flood inducing factors and hazard-affected body；
Law of Disastor Evolution rule is determined according to the coupled relation of each disaster and forms the disaster chain of pipe gallery；
According to the disaster chain building Bayesian network model of formation.
Further, the cloud storage mechanism of the cloud service module includes: using image partition method
Assuming that image segments are stored in K Cloud Server altogether, there is N on i-th of Cloud ServeriA image segments is set
The information content that each image segments include is INFij, each image segments are lost or the probability of leakage is Pij, wherein subscript i table
Show that the segment is located on i-th of Cloud Server, 1≤i≤K, subscript j indicate that the image segments are stored on the Cloud Server
J-th of image segments, 1≤j≤Ni；
The amount of image information that can be then preserved by dividing laggard storage of racking are as follows:
Original image gross information content are as follows:
If the amount of image information that can be preserved without segmentation, under worst case are as follows:
After being split to image, connection is established from different Cloud Servers simultaneously using multithreading, is transmitted, it is false
If transmission total time is T, the time for establishing connection with Cloud Server is Tconnect, transmission time Ttrans, disconnect when
Between be Tdisconnect；
In ameristic situation:
T*=Tconnect+Ttrans+Tdisconnect；
In the case where being split:
T=ALL_Tconnect+ALL_Ttrans+ALL_Tdisconnect；
Wherein, ALL_Tconnect, ALL_Ttrans, ALL_TdisconnectIt is to be built in the case where having segmentation with Cloud Server respectively
Vertical connection, transmission data, the total time disconnected, because being multi-threading parallel process, Tconnect=ALL_Tconnect,
Tdisconnect=ALL_Tdisconnect, ALL_TtransIt can be approximately the transmission time of image segments after dividing, there is N number of image sheet
Section, then have:
ALL_Ttrans=Ttrans/N。
Further, the humidity sensor temperature backoff algorithm of the environmental data detection module are as follows:
Humidity sensor mathematical model are as follows:
Y=f (x, t)；
In formula: x is actual environment relative humidity, and t is temperature parameters, and y humidity sensor exports humidity value；Inverse function are as follows:
X=f-1(y,t)；
Introduce relaxation factor ξi、ξi *And penalty factor, the optimization problem that nonlinear regression is converted to:
In formula: w indicates that weight vector, ε indicate that insensitive loss function coefficients, l indicate sample number；Recycle Lagrange
Multiplier method converts former problem to the Optimization Solution of dual problem:
Wherein, K (xi, xj) it is kernel function, the kernel function used is the Radial basis kernel function of Gaussian distributed, it may be assumed that
K(xi, xj)=exp * | | xi-xj||2/(2*α2)}。
Further, display module optimizes:
If y is original image to be shown, x is the image with noise, and e is noise, then observation model: y=x-e；Image
In noise mathematical model be Guass white noise, so e mean value takes 0, introduce discrete cosine transform formula y=x-e and convert image
It is obtained to frequency domain:
Wherein:
Wherein, f (u, v) is the frequency domain figure coefficient of M × M；
Wavelet transformation is introduced, is converted into frequency domain information, information is made of low frequency component and high fdrequency component, then passes through meter
Calculation is in the horizontal direction, vertical direction, diagonal direction are multiple dimensioned to image development redraws, and steps are as follows for detailed formula: setting to aobvious figure
The two-dimentional scale space of pictureSeparately there is a two-dimentional scale space It isOrthogonal Complementary Set, then have
J is scale coefficient, value range 0,1,2 ..., and has following sequence:
Wherein:
In formula,Respectively represent scale coefficient and wavelet coefficient with ψ, k and m respectively represent coefficient of correspondence matrix row and
Column, then:
In formula, sequence { cj,dj,1,dj,2dj,3It is cj+1Two-dimensional wavelet transformation.
Another object of the present invention is to provide a kind of ground using the underground pipe gallery big data method for visualizing
Lower pipe gallery big data visualization system, the underground pipe gallery big data visualization system include:
Video monitoring module, environmental data detection module, central control module, cloud service module, device management module, wind
Dangerous evaluation module, alarm module, display module；
Video monitoring module is connect with central control module, for monitoring pipe gallery equipment work shape by image pick-up device
State；
Environmental data detection module, connect with central control module, for by sensor detection corridor in temperature, humidity,
Depth of accumulated water, oxygen concentration, H2S concentration and CH4Concentration data information；
Central control module, with video monitoring module, environmental data detection module, cloud service module, device management module,
Risk evaluation module, alarm module, display module connection, work normally for controlling modules by single-chip microcontroller；
Cloud service module, connect with central control module, for concentrating big data resource to detection by Cloud Server
Data are handled；
Device management module is connect with central control module, for passing through Internet of Things integrated management piping lane relevant device；
Risk evaluation module is connect with central control module, for assessing piping lane disaster chain risk；
Alarm module is connect with central control module, for being alarmed by alarm piping lane disaster accident；
Display module is connect with central control module, for the data and monitored picture by display display detection.
Another object of the present invention is to provide a kind of letters using the underground pipe gallery big data method for visualizing
Cease data processing terminal.
Advantages of the present invention and good effect are as follows: the present invention solves equipment goods, materials and equipments essence in pipe gallery by management module
The technical issues of refinement statistics；Environmental data in acquisition in real time and monitoring piping lane, it is convenient to provide for maintenance work, by building
Vertical humidity sensor temperature compensation model, enhances temperature compensation capability, improve humidity sensor measurement accuracy and can
By property；The equipment of security protection, ventilation, power supply, draining, illumination and fire-fighting in piping lane and Cloud Server platform are established into Internet of Things Netcom
Believe framework, technology of Internet of things is utilized, greatly facilitates the management of O&M and equipment, the memory mechanism of cloud service module is carried out
Optimization improves the reliability of storage and the performance of cloud storage；Meanwhile the present invention collects synthesis by risk evaluation module
The risk data of piping lane selectes pipe gallery risk assessment parameter, constructs the Bayesian network model of disaster chain, passes through Bayes
Network model assesses the risk class and probability of happening of each disaster node, identifies drilling for pipe gallery disaster chain according to assessment result
The change stage；Implement corresponding mitigating the disaster by cutting the disaster chain in the headstream measure according to the evolutionary phase of the disaster chain；By to the excellent of display module
Change, improves display resolution.Compared with prior art, the present invention can be according to the evolution of Potential hazards a variety of in pipe gallery
Rule building pipe gallery disaster chain Bayesian network model, the probability and assessment piping lane that each disaster of prediction piping lane occurs lose journey
Degree, to propose scientific and effective pipe gallery mitigating the disaster by cutting the disaster chain in the headstream mechanism and measure.
Detailed description of the invention
Fig. 1 is underground pipe gallery big data visualization system structural schematic diagram provided in an embodiment of the present invention；
In figure: 1, video monitoring module；2, environmental data detection module；3, central control module；4, cloud service module；5,
Device management module；6, risk evaluation module；7, alarm module；8, display module.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, underground pipe gallery big data visualization system provided in an embodiment of the present invention includes: video monitoring
Module 1, environmental data detection module 2, central control module 3, cloud service module 4, device management module 5, risk evaluation module
6, alarm module 7, display module 8.
Video monitoring module 1 is connect with central control module 3, for monitoring the work of pipe gallery equipment by image pick-up device
State；
Environmental data detection module 2 is connect with central control module 3, for passing through temperature, wet in sensor detection corridor
Degree, depth of accumulated water, oxygen concentration, H2S concentration and CH4Concentration data information；
Central control module 3, with video monitoring module 1, environmental data detection module 2, cloud service module 4, equipment management
Module 5, risk evaluation module 6, alarm module 7, display module 8 connect, for controlling the normal work of modules by single-chip microcontroller
Make；
Cloud service module 4 is connect with central control module 3, for concentrating big data resource to detection by Cloud Server
Data handled；
Device management module 5 is connect with central control module 3, for passing through Internet of Things integrated management piping lane relevant device；
Risk evaluation module 6 is connect with central control module 3, for assessing piping lane disaster chain risk；
Alarm module 7 is connect with central control module 3, for being alarmed by alarm piping lane disaster accident；
Display module 8 is connect with central control module 3, for the data and monitored picture by display display detection.
Underground pipe gallery big data method for visualizing provided in an embodiment of the present invention includes:
Step 1, video monitoring module monitor pipe gallery equipment working state using image pick-up device；Environmental data detects mould
Block utilizes temperature, humidity, depth of accumulated water, oxygen concentration, H in sensor detection corridor2S concentration and CH4Concentration data information；
Step 2, central control module concentrate big data resource to the number of detection by cloud service module using Cloud Server
According to being handled；Internet of Things integrated management piping lane relevant device is utilized by device management module；Pass through risk evaluation module pair
Piping lane disaster chain risk is assessed；
Step 3 alarms to piping lane disaster accident using alarm by alarm module；It is utilized by display module
The data and monitored picture of display display detection；
The device management module management method is as follows:
(1) Internet of Things framework is established, Internet of Things framework includes sensing layer, network layer and application layer, and application layer includes that cloud is flat
Platform and WEB page create the virtual three-dimensional model of piping lane in cloud platform；
Sensing layer includes equipment and operation maintenance personnel GPS positioning device in piping lane, and the equipment in the piping lane includes security protection
Equipment, ventilation equipment, power supply unit, drainage equipment, lighting apparatus and fire-fighting equipment；
Network layer includes the access network and communication network of Internet of Things, for security device, ventilation equipment, power supply unit,
Data access and communication between drainage equipment, lighting apparatus and fire-fighting equipment, and make security device, ventilation equipment, power supply
Data interaction is realized between equipment, drainage equipment, lighting apparatus, fire-fighting equipment and cloud platform；
(2) user account database is created in cloud platform, is used for administrative staff's essential information, and mention on the WEB page
For log-in interface；
(3) equipment management data library is created in cloud platform, its step are as follows:
Asset of equipments database is established, record capital budgeting in recent years goes out storage statistics, each system fund accounting, pipeline
Failure rate, maintenance project completion rate, auxiliary device quantity statistics, equipment fault statistics and equipment scrapping statistics；
Attribute information is established for all devices in piping lane, and creates index；The attribute information includes device numbering, sets
Standby type, device name, device model, parameter, measurement unit, support area and support time；
Equipment is provided in WEB page and adds interface, user adds necessary the believing of interface input newly added equipment by equipment
Breath, after user has inputted the necessary information of newly added equipment, cloud platform adds the number of newly-increased equipment in equipment management data library
According to；Necessary information includes device numbering, device type, device name, device model, parameter, measurement unit and support area；
(4) wisdom monitor database and wisdom are created and monitor WEB page, wisdom monitor database record fire-fighting system,
Ventilating system, power supply system, lighting system, the working condition of drainage system and security system；With the side of chart in WEB page
Formula shows the operating state data of fire-fighting system, ventilating system, power supply system, lighting system, drainage system or security system；
(5) inspection management database and inspection management WEB page, inspection management data-base recording Daily Round Check letter are created
Breath, workform management data and patrol officer's location information, and Daily Round Check is graphically shown in inspection management WEB page
Information, workform management data and patrol officer's location information；
(6) contingency management database and contingency management WEB page are created, all emergency of contingency management data-base recording are pre-
Case, contingency management WEB page show all emergency preplans；
(7) O&M knowledge data base is created, records the relevant knowledge and experience of piping lane O&M, and pass through WEB page presentation
The relevant knowledge and experience of piping lane O&M；In creation O&M report database, the O&M report in per January or year is recorded, and is passed through
WEB page shows the O&M report in per January or year.
Further, the risk evaluation module appraisal procedure is as follows:
Firstly, monitoring the risk assessment parameter that risk data determines pipe gallery, the O&M according to pipe gallery O&M
Monitoring risk data includes environmental monitoring data corresponding to the alert event or Disaster Event that occur during pipe gallery O&M
And the frequency occurred；
Secondly, constructing the Bayesian network model of pipe gallery disaster chain according to the risk assessment parameter；
Then, the risk assessment of pipe gallery disaster chain is carried out according to the Bayesian network model；
Finally, implementing corresponding mitigating the disaster by cutting the disaster chain in the headstream measure according to assessment result.
Further, described that the risk assessment parameter packet that risk data determines pipe gallery is monitored according to pipe gallery O&M
It includes:
The collection of risk data is carried out, the risk data includes the alert event occurred during pipe gallery O&M or calamity
Environmental monitoring data and the frequency of generation corresponding to evil event；
Flood inducing factors and corresponding hazard-affected body are selected according to the risk data of acquisition and factors causing disaster.
Further, the Bayesian network model packet that pipe gallery disaster chain is constructed according to the risk assessment parameter
It includes:
The coupled relation of each disaster is determined according to selected Flood inducing factors and hazard-affected body；
Law of Disastor Evolution rule is determined according to the coupled relation of each disaster and forms the disaster chain of pipe gallery；
According to the disaster chain building Bayesian network model of formation.
Further, the cloud storage mechanism of the cloud service module includes: using image partition method
Assuming that image segments are stored in K Cloud Server altogether, there is N on i-th of Cloud ServeriA image segments is set
The information content that each image segments include is INFij, each image segments are lost or the probability of leakage is Pij, wherein subscript i table
Show that the segment is located on i-th of Cloud Server, 1≤i≤K, subscript j indicate that the image segments are stored on the Cloud Server
J-th of image segments, 1≤j≤Ni；
The amount of image information that can be then preserved by dividing laggard storage of racking are as follows:
Original image gross information content are as follows:
If the amount of image information that can be preserved without segmentation, under worst case are as follows:
After being split to image, connection is established from different Cloud Servers simultaneously using multithreading, is transmitted, it is false
If transmission total time is T, the time for establishing connection with Cloud Server is Tconnect, transmission time Ttrans, disconnect when
Between be Tdisconnect；
In ameristic situation:
T*=Tconnect+Ttrans+Tdisconnect；
In the case where being split:
T=ALL_Tconnect+ALL_Ttrans+ALL_Tdisconnect；
Wherein, ALL_Tconnect, ALL_Ttrans, ALL_Tdisconnect；Be respectively have segmentation in the case where with Cloud Server
It establishes connection, transmits data, the total time disconnected, because being multi-threading parallel process, Tconnect=ALL_
Tconnect, Tdisconnect=ALL_Tdisconnect, ALL_TtransIt can be approximately the transmission time of image segments after dividing, there is N number of figure
Photo section, then have:
ALL_Ttrans=Ttrans/N。
Further, the humidity sensor temperature backoff algorithm of the environmental data detection module are as follows:
Humidity sensor mathematical model are as follows:
Y=f (x, t)；
In formula: x is actual environment relative humidity, and t is temperature parameters, and y humidity sensor exports humidity value；Inverse function are as follows:
X=f-1(y,t)；
Introduce relaxation factor ξi、And penalty factor, the optimization problem that nonlinear regression is converted to:
In formula: w indicates that weight vector, ε indicate that insensitive loss function coefficients, l indicate sample number；Recycle Lagrange
Multiplier method converts former problem to the Optimization Solution of dual problem:
Wherein, K (xi, xj) it is kernel function, the kernel function used is the Radial basis kernel function of Gaussian distributed, it may be assumed that
K(xi, xj)=exp-| | xi-xj||2/(2*α2)}。
Further, display module optimizes:
If y is original image to be shown, x is the image with noise, and e is noise, then observation model: y=x-e；Image
In noise mathematical model be Guass white noise, so e mean value takes 0, introduce discrete cosine transform formula y=x-e and convert image
It is obtained to frequency domain:
Wherein:
Wherein, f (u, v) is the frequency domain figure coefficient of M × M；
Wavelet transformation is introduced, is converted into frequency domain information, information is made of low frequency component and high fdrequency component, then passes through meter
Calculation is in the horizontal direction, vertical direction, diagonal direction are multiple dimensioned to image development redraws, and steps are as follows for detailed formula: setting to aobvious figure
The two-dimentional scale space of pictureSeparately there is a two-dimentional scale space It isOrthogonal Complementary Set, then have
J is scale coefficient, value range 0,1,2 ..., and has following sequence:
Wherein:
In formula,Respectively represent scale coefficient and wavelet coefficient with ψ, k and m respectively represent coefficient of correspondence matrix row and
Column, then:
。
Claims (9)
1. a kind of underground pipe gallery big data method for visualizing, which is characterized in that the underground pipe gallery big data is visual
Change method includes:
Step 1, video monitoring module monitor pipe gallery equipment working state using image pick-up device；Environmental data detection module benefit
With temperature, humidity, depth of accumulated water, oxygen concentration, H in sensor detection corridor2S concentration and CH4Concentration data information；
Step 2, central control module by cloud service module using Cloud Server concentrate big data resource to the data of detection into
Row processing；Internet of Things integrated management piping lane relevant device is utilized by device management module；By risk evaluation module to piping lane
Disaster chain risk is assessed；
Step 3 alarms to piping lane disaster accident using alarm by alarm module；Display is utilized by display module
The data and monitored picture of device display detection；
The device management module management method is as follows:
(1) establish Internet of Things framework, Internet of Things framework includes sensing layer, network layer and application layer, application layer include cloud platform and
WEB page creates the virtual three-dimensional model of piping lane in cloud platform；
Sensing layer includes equipment and operation maintenance personnel GPS positioning device in piping lane, the equipment in the piping lane include security device,
Ventilation equipment, power supply unit, drainage equipment, lighting apparatus and fire-fighting equipment；
Network layer includes the access network and communication network of Internet of Things, is used for security device, ventilation equipment, power supply unit, draining
Data access and communication between equipment, lighting apparatus and fire-fighting equipment, and make security device, ventilation equipment, power supply unit,
Data interaction is realized between drainage equipment, lighting apparatus, fire-fighting equipment and cloud platform；
(2) user account database is created in cloud platform, is used for administrative staff's essential information, and provide and step in WEB page
Lithosphere face；
(3) equipment management data library is created in cloud platform, its step are as follows:
Asset of equipments database is established, record capital budgeting in recent years goes out storage statistics, each system fund accounting, pipeline failure
Rate, maintenance project completion rate, auxiliary device quantity statistics, equipment fault statistics and equipment scrapping statistics；
Attribute information is established for all devices in piping lane, and creates index；The attribute information includes device numbering, equipment class
Type, device name, device model, parameter, measurement unit, support area and support time；
Equipment is provided in WEB page and adds interface, user adds the necessary information that interface inputs newly added equipment by equipment, uses
After family has inputted the necessary information of newly added equipment, cloud platform adds the data of newly-increased equipment in equipment management data library；It must
Wanting information includes device numbering, device type, device name, device model, parameter, measurement unit and support area；
(4) it creates wisdom monitor database and wisdom monitors WEB page, record fire-fighting system, ventilation in wisdom monitor database
System, power supply system, lighting system, the working condition of drainage system and security system；It is graphically opened up in WEB page
Show the operating state data of fire-fighting system, ventilating system, power supply system, lighting system, drainage system or security system；
(5) inspection management database and inspection management WEB page, inspection management data-base recording Daily Round Check information, work are created
Menu manager data and patrol officer's location information, and inspection management WEB page graphically show Daily Round Check information,
Workform management data and patrol officer's location information；
(6) it creates contingency management database and contingency management WEB page, all emergency preplans of contingency management data-base recording is answered
Anxious management WEB page shows all emergency preplans；
(7) O&M knowledge data base is created, the relevant knowledge and experience of piping lane O&M are recorded, and piping lane is shown by WEB page
The relevant knowledge and experience of O&M；In creation O&M report database, the O&M report in per January or year is recorded, and passes through WEB
The report of the O&M in page presentation per January or year.
2. underground pipe gallery big data method for visualizing as described in claim 1, which is characterized in that the risk assessment mould
Block appraisal procedure is as follows:
Firstly, monitoring the risk assessment parameter that risk data determines pipe gallery, the O&M monitoring according to pipe gallery O&M
Risk data includes environmental monitoring data and hair corresponding to the alert event or Disaster Event that occur during pipe gallery O&M
Raw frequency；
Secondly, constructing the Bayesian network model of pipe gallery disaster chain according to the risk assessment parameter；
Then, the risk assessment of pipe gallery disaster chain is carried out according to the Bayesian network model；
Finally, implementing corresponding mitigating the disaster by cutting the disaster chain in the headstream measure according to assessment result.
3. underground pipe gallery big data method for visualizing as claimed in claim 2, which is characterized in that described according to integrated pipe
Corridor O&M monitoring risk data determines that the risk assessment parameter of pipe gallery includes:
The collection of risk data is carried out, the risk data includes the alert event occurred during pipe gallery O&M or disaster thing
Environmental monitoring data corresponding to part and the frequency of generation；
Flood inducing factors and corresponding hazard-affected body are selected according to the risk data of acquisition and factors causing disaster.
4. underground pipe gallery big data method for visualizing as claimed in claim 2, which is characterized in that described according to the wind
Nearly the Bayesian network model of assessment parameter building pipe gallery disaster chain includes:
The coupled relation of each disaster is determined according to selected Flood inducing factors and hazard-affected body；
Law of Disastor Evolution rule is determined according to the coupled relation of each disaster and forms the disaster chain of pipe gallery；
According to the disaster chain building Bayesian network model of formation.
5. underground pipe gallery big data method for visualizing as described in claim 1, which is characterized in that the cloud service module
Cloud storage mechanism include: using image partition method
Assuming that image segments are stored in K Cloud Server altogether, there is N on i-th of Cloud ServeriA image segments sets each figure
The information content that photo section includes is INFij, each image segments are lost or the probability of leakage is Pij, wherein subscript i indicates the piece
Section is located on i-th of Cloud Server, and 1≤i≤K, subscript j indicates j-th that the image segments are stored on the Cloud Server
Image segments, 1≤j≤Ni；
The amount of image information that can be then preserved by dividing laggard storage of racking are as follows:
Original image gross information content are as follows:
If the amount of image information that can be preserved without segmentation, under worst case are as follows:
After being split to image, connection is established from different Cloud Servers simultaneously using multithreading, is transmitted, it is assumed that pass
Defeated total time is T, and the time for establishing connection with Cloud Server is Tconnect, transmission time Ttrans, the time disconnected is
Tdisconnect；
In ameristic situation:
T*=Tconnect+Ttrans+Tdisconnect；
In the case where being split:
T=ALL_Tconnect+ALL_Ttrans+ALL_Tdisconnect；
Wherein, ALL_Tconnect, ALL_Ttrans, ALL_TdisconnectIt is to establish to connect with Cloud Server in the case where having segmentation respectively
It connects, transmission data, the total time disconnected, because being multi-threading parallel process, Tconnect=ALL_Tconnect,
Tdisconnect=ALL_Tdisconnect, ALL_TtransIt can be approximately the transmission time of image segments after dividing, there is N number of image sheet
Section, then have:
ALL_Ttrans=Ttrans/N。
6. underground pipe gallery big data method for visualizing as described in claim 1, which is characterized in that the environmental data inspection
Survey the humidity sensor temperature backoff algorithm of module are as follows:
Humidity sensor mathematical model are as follows:
Y=f (x, t)；
In formula: x is actual environment relative humidity, and t is temperature parameters, and y humidity sensor exports humidity value；Inverse function are as follows:
X=f-1(y,t)；
Introduce relaxation factor ξi、ξi *And penalty factor, the optimization problem that nonlinear regression is converted to:
In formula: w indicates that weight vector, ε indicate that insensitive loss function coefficients, l indicate sample number；Recycle Lagrange multiplier
Method converts former problem to the Optimization Solution of dual problem:
Wherein, K (xi, xj) it is kernel function, the kernel function used is the Radial basis kernel function of Gaussian distributed, it may be assumed that
K(xi, xj)=exp-| | xi-xj||2/(2*α2)}。
7. underground pipe gallery big data method for visualizing as described in claim 1, which is characterized in that display module carries out excellent
Change:
If y is original image to be shown, x is the image with noise, and e is noise, then observation model: y=x-e；In image
Noise mathematical model is Guass white noise, so e mean value takes 0, introduces discrete cosine transform formula y=x-e for image and transforms to frequency
Domain obtains:
Wherein:
Wherein, f (u, v) is the frequency domain figure coefficient of M × M；
Wavelet transformation is introduced, frequency domain information is converted into, information is made of low frequency component and high fdrequency component, then by calculating
Horizontal direction, vertical direction, diagonal direction are multiple dimensioned to image development to be redrawn, and steps are as follows for detailed formula: setting image to be shown
Two-dimentional scale spaceSeparately there is a two-dimentional scale space It isOrthogonal Complementary Set, then haveJ is
Scale coefficient, value range 0,1,2 ..., and have following sequence:
Wherein:
In formula,Scale coefficient and wavelet coefficient are respectively represented with ψ, k and m respectively represent the row and column of coefficient of correspondence matrix, then:
In formula, sequence { cj,dj,1,dj,2dj,3It is cj+1Two-dimensional wavelet transformation.
8. a kind of underground pipe gallery big data using underground pipe gallery big data method for visualizing described in claim 1 can
Depending on change system, which is characterized in that the underground pipe gallery big data visualization system includes:
Video monitoring module, environmental data detection module, central control module, cloud service module, device management module, risk are commented
Estimate module, alarm module, display module；
Video monitoring module is connect with central control module, for monitoring pipe gallery equipment working state by image pick-up device；
Environmental data detection module, connect with central control module, for detecting temperature, humidity, ponding in corridor by sensor
Depth, oxygen concentration, H2S concentration and CH4Concentration data information；
Central control module, with video monitoring module, environmental data detection module, cloud service module, device management module, risk
Evaluation module, alarm module, display module connection, work normally for controlling modules by single-chip microcontroller；
Cloud service module, connect with central control module, for concentrating big data resource to the data of detection by Cloud Server
It is handled；
Device management module is connect with central control module, for passing through Internet of Things integrated management piping lane relevant device；
Risk evaluation module is connect with central control module, for assessing piping lane disaster chain risk；
Alarm module is connect with central control module, for being alarmed by alarm piping lane disaster accident；
Display module is connect with central control module, for the data and monitored picture by display display detection.
9. a kind of information data using underground pipe gallery big data method for visualizing described in claim 1~7 any one
Processing terminal.
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110243883A (en) * | 2019-06-18 | 2019-09-17 | 福州大学 | A kind of relative humidity approximating method suitable for the intelligent humidity sensor-based system based on HS1101 |
| 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 |
| CN110397080A (en) * | 2019-07-17 | 2019-11-01 | 深圳万海建筑工程科技有限公司 | A kind of monitoring and warning system for pipe gallery |
| CN110488681A (en) * | 2019-07-26 | 2019-11-22 | 合肥森弗卡电子科技有限公司 | A kind of network-based underpass safety and protection system |
| CN110737231A (en) * | 2019-11-04 | 2020-01-31 | 江苏安防科技有限公司 | piping lane monitoring system based on internet of things and three-dimensional visualization technology |
| CN111538297A (en) * | 2020-03-17 | 2020-08-14 | 衢州学院 | Web-based interactive three-dimensional visualization platform |
| CN111582674A (en) * | 2020-04-22 | 2020-08-25 | 北京启安智慧科技有限公司 | Natech event disaster chain evolution analysis system and method |
| CN112215373A (en) * | 2020-10-22 | 2021-01-12 | 河南正兴工程管理有限公司 | Underground pipe gallery construction detection management system and method |
| CN112446077A (en) * | 2020-11-19 | 2021-03-05 | 河南汇祥通信设备有限公司 | Comprehensive pipe rack operation and maintenance risk assessment system and method based on big data |
| CN112613639A (en) * | 2020-11-30 | 2021-04-06 | 河南汇祥通信设备有限公司 | System and method for predicting operation and maintenance data of comprehensive pipe rack based on LSTM model |
| CN112633685A (en) * | 2020-12-23 | 2021-04-09 | 河南汇祥通信设备有限公司 | Comprehensive pipe rack operation and maintenance risk management and control system and method based on image analysis |
| CN112862147A (en) * | 2020-11-19 | 2021-05-28 | 河南汇祥通信设备有限公司 | Comprehensive pipe rack operation and maintenance risk evaluation model and method based on BP neural network |
| CN113255717A (en) * | 2021-03-25 | 2021-08-13 | 中冶赛迪重庆信息技术有限公司 | Piping lane fire detection method and system |
| CN113326964A (en) * | 2020-12-01 | 2021-08-31 | 河南汇祥通信设备有限公司 | Data composite multistage comprehensive pipe rack risk management and control method and method |
| CN114022970A (en) * | 2021-08-09 | 2022-02-08 | 中建二局第三建筑工程有限公司 | Intelligent electric box inspection system and method |
| CN114783126A (en) * | 2022-04-22 | 2022-07-22 | 中煤科工集团重庆智慧城市科技研究院有限公司 | Illegal intrusion control management system for tunnel pipe gallery |
| CN115081340A (en) * | 2022-07-23 | 2022-09-20 | 北京云庐科技有限公司 | Micro pipe gallery gas monitoring method and system based on simulation and deep learning |
| CN115099482A (en) * | 2022-06-21 | 2022-09-23 | 杭州水务数智科技股份有限公司 | Underground pipeline and pipe gallery multi-state detection method |
| CN118297409A (en) * | 2024-06-06 | 2024-07-05 | 中交路桥科技有限公司 | Urban underground security risk assessment method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107016502A (en) * | 2017-03-30 | 2017-08-04 | 中建地下空间有限公司 | A kind of pipe gallery intelligent management system |
| CN107437153A (en) * | 2017-08-14 | 2017-12-05 | 长沙变化率信息技术有限公司 | Underground pipe gallery big data visualized O＆M cloud platform |
| CN107798401A (en) * | 2017-12-01 | 2018-03-13 | 南京智行信息科技有限公司 | A kind of pipe gallery wisdom management method based on Internet of Things |
| CN108062638A (en) * | 2018-02-09 | 2018-05-22 | 国通广达（北京）技术有限公司 | Pipe gallery disaster chain methods of risk assessment |
| CN108295407A (en) * | 2017-12-21 | 2018-07-20 | 山东康威通信技术股份有限公司 | Robot cable piping lane scene fire alarm and extinguishing method, device, system |
-
2018
- 2018-10-19 CN CN201811221089.9A patent/CN109408686A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107016502A (en) * | 2017-03-30 | 2017-08-04 | 中建地下空间有限公司 | A kind of pipe gallery intelligent management system |
| CN107437153A (en) * | 2017-08-14 | 2017-12-05 | 长沙变化率信息技术有限公司 | Underground pipe gallery big data visualized O＆M cloud platform |
| CN107798401A (en) * | 2017-12-01 | 2018-03-13 | 南京智行信息科技有限公司 | A kind of pipe gallery wisdom management method based on Internet of Things |
| CN108295407A (en) * | 2017-12-21 | 2018-07-20 | 山东康威通信技术股份有限公司 | Robot cable piping lane scene fire alarm and extinguishing method, device, system |
| CN108062638A (en) * | 2018-02-09 | 2018-05-22 | 国通广达（北京）技术有限公司 | Pipe gallery disaster chain methods of risk assessment |
Non-Patent Citations (3)
| Title |
|---|
| 吕骁博，郭耀，陈向群: "基于分割的数字图像云存储机制", 《计算机研究与发展》 * |
| 姜力，贺晓雷，行鸿彦: "改进GA-SVM 的湿度传感器温度补偿研究", 《电子测量与仪器学报》 * |
| 蔡黎，代妮娜，戴闽鲁，邓明，邱刚，王欣煜: "车载交互系统TFT- LCD 显示优化算法设计", 《电视技术》 * |
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| CN114783126A (en) * | 2022-04-22 | 2022-07-22 | 中煤科工集团重庆智慧城市科技研究院有限公司 | Illegal intrusion control management system for tunnel pipe gallery |
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