CN110503793A - A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence - Google Patents
A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence Download PDFInfo
- Publication number
- CN110503793A CN110503793A CN201910680176.9A CN201910680176A CN110503793A CN 110503793 A CN110503793 A CN 110503793A CN 201910680176 A CN201910680176 A CN 201910680176A CN 110503793 A CN110503793 A CN 110503793A
- Authority
- CN
- China
- Prior art keywords
- video
- early warning
- unit
- pipeline
- information
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19639—Details of the system layout
- G08B13/19645—Multiple cameras, each having view on one of a plurality of scenes, e.g. multiple cameras for multi-room surveillance or for tracking an object by view hand-over
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19654—Details concerning communication with a camera
- G08B13/1966—Wireless systems, other than telephone systems, used to communicate with a camera
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Alarm Systems (AREA)
Abstract
A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence provided herein, which comprises obtain the monitor video and location information of pipe-line equipment position；It detects in the monitor video with the presence or absence of intrusion behavior；If so, generating pipeline attack early warning information and carrying out short message early warning by Beidou signal transceiver；It whether there is intrusion behavior in the monitor video that the application passes through detection pipe-line equipment position, short message early warning is carried out by Beidou signal transceiver, fast automatic early warning and early warning position can be navigated to, compared to traditional artificial routine inspection mode, real-time is high, convenient for the implementation of emergency preplan, compared to the mode that tradition relies on vibrating sensor inspection, using the computer vision technique in more forward position, can more accurately early warning, rate of false alarm is effectively reduced.
Description
Technical field
This application involves pipe-line equipment safety detection technology fields, enter more particularly, to a kind of pipeline based on artificial intelligence
Invade automatic early warning method, system and equipment.
Background technique
The gas pipeline of gas pipeline especially long distance delivery is the most important conveying equipment of combustion gas, and China is vast in territory
And West-East National Gas Transmission Project span is very big, proposes arduous requirement and demand to the inspection monitoring protection of gas pipeline.Length is defeated
In 5 meters of gas pipeline two sides must not exist construction or other destroy pipelines behavior, pipeline is once destroyed leak if,
Significant threat will be generated to safety.
Sensor and manual type are relied primarily on for the routine inspection mode of long distance pipeline at present.In the mode of sensor inspection
There are two main classes for the sensor of use: the working principle of fibre optical sensor and vibrating sensor, these two types of sensors is dependent on pipe
Vibration around road infers whether to be invaded, and is extremely easy wrong report, brings many unnecessary troubles to repair personnel.And people
Although the mode of work inspection is not reported by mistake, since the speed of inspection is slow, of long duration, and can not find to invade at the first time
It destroys situation and delays the job schedule of repairing.
Therefore, it is all based on after pipeline is destroyed by invasion based on current existing detection device and a degree of leakage occurs
It just alarms afterwards, and polling rate is slow, rate of false alarm is high, needs a kind of pipeline invasion automatic early warning method based on artificial intelligence, is
System and equipment, to realize that real-time, rate of false alarm is low, pipeline invades automatic early-warning.
Summary of the invention
In view of the deficiencies of the prior art, the application provide it is a kind of based on artificial intelligence pipeline invasion automatic early warning method,
System and equipment solve the problems such as inspection timeliness is low, rate of false alarm is high after pipeline is invaded in the prior art.
In order to solve the above technical problems, the application provides a kind of pipeline invasion automatic early warning method based on artificial intelligence,
Include:
Obtain the monitor video and location information of pipe-line equipment position；
It detects in the monitor video with the presence or absence of intrusion behavior；
If so, generating pipeline attack early warning information and carrying out short message early warning by Beidou signal transceiver.
Preferably, it whether there is intrusion behavior in the detection monitor video, comprising:
The monitor video is subjected to task distribution processor；
Monitor video progress video decoding process is obtained into decoding video；
Judge whether the load of video processor is idle, if then loading the decoding video to video processor；
Being detected using the preset deep learning algorithm of video processor whether there is intrusion behavior in the decoding video；
If so, the video interception and video clip of storage monitor video.[AZ1]
It is furthermore preferred that described whether there is using the video processor preset deep learning algorithm detection decoding video
Intrusion behavior, comprising:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometry change will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
It changes, and characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile of intrusion target
Information；
It is detected in the decoding video according to the position of the intrusion target and motion track information with the presence or absence of invasion row
For.
Preferably, the generation pipeline attack early warning information and by Beidou signal transceiver carry out short message early warning, packet
It includes:
The corresponding monitoring device number of the monitor video and pipe-line equipment invasion location information are generated into warning information；
Warning information to remote back-office, which is sent, by Beidou signal transceiver carries out short message early warning.
The application also provides a kind of pipeline invasion automatic early-warning system based on artificial intelligence, comprising:
Acquiring unit, the acquiring unit are configured to obtain the monitor video of pipe-line equipment position and position letter
Breath；
Detection unit, the detection unit are configured to detect the monitor video with the presence or absence of intrusion behavior；
Prewarning unit, the prewarning unit are configured to intrusion behavior if it exists, then generate pipeline attack early warning information simultaneously
Short message early warning is carried out by Beidou signal transceiver.
Preferably, the system also includes power supply unit, said supply unit is configured to as system power supply.
Preferably, the detection unit specifically includes:
Route distribution unit, the route distribution processing unit are configured to the monitor video carrying out task Issuing Office
Reason；
Video decoding unit, the video decoding unit are configured to obtain monitor video progress video decoding process
To decoding video；
Load Balance Unit, the Load Balance Unit is for judging whether the load of video processor is idle, if then
The decoding video is loaded to video processor；
Computing unit, the computing unit are configured to using described in the preset deep learning algorithm judgement of video processor
It whether there is intrusion behavior in decoding video；
Storage unit, the memory cell arrangements are used to store the video interception and view of the monitor video there are intrusion behavior
Frequency segment.
Preferably, the computing unit is specifically used for:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometry change will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
It changes, and characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile of intrusion target
Information；
It is detected in the decoding video according to the position of the intrusion target and motion track information with the presence or absence of invasion row
For.[AZ2]
Preferably, the prewarning unit includes:
Generation unit, the generation unit are configured to set the corresponding monitoring device number of the monitor video and pipeline
Standby invasion location information generates warning information；
Transmission unit, the transmission unit are configured to Beidou signal transceiver and send warning information to remote back-office
Carry out short message early warning.
The present invention also provides invade automatic early-warning equipment based on a kind of pipeline based on artificial intelligence, comprising:
Monitoring device, edge detection equipment, Beidou signal transceiver and power supply unit,
Wherein, the monitoring device is connect with edge detection equipment, for monitor pipe-line equipment and by monitor video it is real-time
It is sent to edge detection equipment；
The edge detection equipment is connect with Beidou signal transceiver, for detect whether there is in the monitor video into
Invade behavior, and if it exists, then generate pipeline attack early warning information and be sent to Beidou signal transceiver；
The Beidou signal transceiver carries out short message early warning for receiving pipeline attack early warning information and sends the position
Confidence breath；
The power supply unit connect power supply with monitoring device, edge detection unit and Beidou signal transceiver.
Preferably, the monitoring device is made of 4-20 independent high definition monitoring cameras.
Preferably, the edge detection equipment specifically includes: main control unit, computing module and memory module, the master control
Unit includes: routing module, Video decoding module, MCU module；The computing unit includes AP module and NP processing module.
Preferably, the power supply unit includes: solar panel and the built-in energy storage that is connected with the solar panel
Battery, power supply of the output end of the built-in energy-storage battery as monitoring device, edge detection equipment and Beidou signal transceiver
End.
It whether there is intrusion behavior in the monitor video that the application passes through detection pipe-line equipment position, believed by Beidou
Number transceiver carries out short message early warning, fast automatic early warning and can navigate to early warning position, compared to traditional artificial routine inspection mode,
Real-time is high, convenient for the implementation of emergency preplan, compared to the mode that tradition relies on vibrating sensor inspection, using more forward position
Computer vision technique, can more accurately early warning, rate of false alarm is effectively reduced.
In addition, the application reduces operation intrusion detection algorithm institute using instantly popular DSA (domain-specific) chip architecture
The power consumption needed；The application is not depended on using the publication that Beidou carries out attack early warning information using Beidou message as information carrier
Mobile communication and internet communication can solve nobody remote communication issue without 2/3/4G signal covering area, and have essence
Quasi- positioning function and short message function, can be realized the publication of warning information and the quick positioning of early warning position；The application adopts
It is solar powered with clean energy resource, meet the power demand of integral device, not only solves long distance pipeline local environment power pole
For the rare problem of inconvenient, power supply facilities, and environmentally friendly, has and sustainable use advantage.Due to the peace of integral device
Dress mode is non-intruding duct type, and cost is relatively low for installing engineering, it can be achieved that rapid deployment.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of pipeline invasion automatic early warning method process based on artificial intelligence provided by the embodiment of the present application
Figure；
Fig. 2 invades automatic early-warning system structure for a kind of pipeline based on artificial intelligence provided by the embodiment of the present application and shows
It is intended to；
Fig. 3 is a kind of pipeline invasion automatic early-warning device structure group based on artificial intelligence provided by the embodiment of the present application
At schematic diagram；
Fig. 4 is edge detection equipment DSA chip carrier composition provided by the embodiment of the present application；
Fig. 5 is a kind of pipeline invasion automatic early-warning system work fortune based on artificial intelligence provided by the embodiment of the present application
Row flow chart.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall in the protection scope of this application.
The Key Term occurred in the application is explained below.
DSA:Domain Specific Architecture, domain-specific framework；
MCU:Microcontroller Unit, micro-control unit；
AP:Adjunct Processor, auxiliary processor；
NP:Neural Network Processor, neural network application specific processor.
Referring to FIG. 1, Fig. 1 is a kind of pipeline invasion automatic early-warning based on artificial intelligence provided by the embodiment of the present application
Method flow diagram, the method for early warning 100 include:
S101: the monitor video and location information of pipe-line equipment position are obtained；
S102: it detects in the monitor video with the presence or absence of intrusion behavior；
S103: if so, generating pipeline attack early warning information and carrying out short message early warning by Beidou signal transceiver.
It should be noted that intrusion behavior includes but is not limited to: artificial excavation, piling machine are excavated, excavator excavates or ground
Shake interference.
Based on the above embodiment, as preferred embodiment, the step 102 detects whether there is in the monitor video
Intrusion behavior, comprising:
The monitor video is subjected to task distribution processor；
Monitor video progress video decoding process is obtained into decoding video；
Judge whether the load of video processor is idle, if then loading the decoding video to video processor；
Being detected using the preset deep learning algorithm of video processor whether there is intrusion behavior in the decoding video；
If so, the video interception and video clip of storage monitor video.
Based on the above embodiment, described to utilize the preset deep learning algorithm of video processor as preferred embodiment
The decoding video is detected with the presence or absence of intrusion behavior, comprising:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometry change will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
It changes, and characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile of intrusion target
Information；
It is detected in the decoding video according to the position of the intrusion target and motion track information with the presence or absence of invasion row
For.
Based on the above embodiment, as preferred embodiment, the step 103 generates pipeline attack early warning information and passes through
Beidou signal transceiver carries out short message early warning, comprising:
The corresponding monitoring device number of the monitor video and pipe-line equipment invasion location information are generated into warning information；
Warning information to remote back-office, which is sent, by Beidou signal transceiver carries out short message early warning.
It should be noted that the warning information may also include the invasion time, invasion determines the key messages such as result.
Specifically, the pipeline invasion automatic early warning method based on artificial intelligence includes:
Obtain the monitor video and location information of pipe-line equipment position；
The monitor video is subjected to task distribution processor；
Monitor video progress video decoding process is obtained into decoding video；
Judge whether the load of video processor is idle, if then loading the decoding video to video processor；
Being detected using the preset deep learning algorithm of video processor whether there is intrusion behavior in the decoding video；
If so, the video interception and video clip of storage monitor video.
The image data characteristic information of decoding video is extracted using YOLO v3 deep learning algorithm；
Geometry change will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
It changes, and characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile of intrusion target
Information；
It is detected in the decoding video according to the position of the intrusion target and motion track information with the presence or absence of invasion row
For；
If so, the corresponding monitoring device number of the monitor video and pipe-line equipment invasion location information are generated early warning
Information；
Warning information to remote back-office, which is sent, by Beidou signal transceiver carries out short message early warning.
Referring to FIG. 2, a kind of pipeline invasion automatic early-warning system based on artificial intelligence provided by Fig. 2 the embodiment of the present application
System structural schematic diagram, the early warning system 200 include:
Acquiring unit 201, the acquiring unit 201 are configured to obtain monitor video and the position of pipe-line equipment position
Confidence breath；
Detection unit 202, the detection unit 202 are configured to detect the monitor video with the presence or absence of intrusion behavior；
Prewarning unit 203, the prewarning unit 203 are configured to intrusion behavior if it exists, generate pipeline attack early warning letter
It ceases and passes through Beidou signal transceiver and carry out short message early warning.
Based on the above embodiment, as preferred embodiment, the system also includes power supply unit, said supply unit is matched
It sets for being system power supply.
Based on the above embodiment, as preferred embodiment, the detection unit 202 is specifically included:
Route distribution unit, the route distribution processing unit are configured to the monitor video carrying out task Issuing Office
Reason；
Video decoding unit, the video decoding unit are configured to obtain monitor video progress video decoding process
To decoding video；
Load Balance Unit, the Load Balance Unit is for judging whether the load of video processor is idle, if then
The decoding video is loaded to video processor；
Computing unit, the computing unit are configured to using described in the preset deep learning algorithm judgement of video processor
It whether there is intrusion behavior in decoding video；
Storage unit, the memory cell arrangements are used to store the video interception and view of the monitor video there are intrusion behavior
Frequency segment.
Based on the above embodiment, as preferred embodiment, the computing unit is specifically used for:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometry change will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
It changes, and characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile of intrusion target
Information；
It is detected in the decoding video according to the position of the intrusion target and motion track information with the presence or absence of invasion row
For.
Based on the above embodiment, as preferred embodiment, the prewarning unit 203 includes:
Generation unit, the generation unit are configured to set the corresponding monitoring device number of the monitor video and pipeline
Standby invasion location information generates warning information；
Transmission unit, the transmission unit are configured to Beidou signal transceiver and send warning information to remote back-office
Carry out short message early warning.
Referring to FIG. 3, provided by Fig. 3 the embodiment of the present application it is a kind of based on artificial intelligence pipeline invasion automatic early-warning set
Standby structure composition schematic diagram, the source of early warning 300 include:
Monitoring device 301, edge detection equipment 302, Beidou signal transceiver 303 and power supply unit 304,
Wherein, the monitoring device 301 is connect with edge detection equipment 302, for monitoring pipe-line equipment and regarding monitoring
Frequency is sent to edge detection equipment 302 in real time；
The edge detection equipment 302 is connect with Beidou signal transceiver 303, for detect in the monitor video whether
There are intrusion behaviors, and if it exists, then generates pipeline attack early warning information and is sent to Beidou signal transceiver 303；
The Beidou signal transceiver 303 carries out described in short message early warning and transmission for receiving pipeline attack early warning information
Location information；
The power supply unit 304 connect confession with monitoring device 301, edge detection unit 302 and Beidou signal transceiver 303
Electricity.
Based on the above embodiment, as preferred embodiment, the monitoring device is taken the photograph by 4-20 independent high-definition monitorings
As head forms.
Based on the above embodiment, as preferred embodiment, the edge detection equipment is specifically included: main control unit, meter
Module, memory module are calculated, the main control unit includes: routing module, Video decoding module, MCU module；The computing unit packet
Include AP module and np module.
Specifically, edge detection equipment DSA chip carrier composition is as shown in figure 4, as shown in Figure 4, edge detection equipment is based on
It customizes DSA chip to realize, carries the related algorithms such as deep learning, inside includes main control unit, computing module and memory module.It is main
Controlling unit includes routing module, Video decoding module and MCU module, and computing unit includes np module and AP module, wherein routing
Module is responsible for task distribution processor, and Video decoding module is responsible for the video acquired to multiple cameras and carries out video decoding process,
MCU module is responsible for load balancing and judges whether np module loads the free time, and AP module is responsible for loading video to np module, np module
It is responsible for operation deep learning algorithm and optimal frames selection, cutting, invasion judgement is carried out to video.Storage unit is responsible for storing related figure
Piece and video clip, when algorithm is judged to invading, the picture and video clip of interception then classification storage in memory module, for
Inquiry is transferred.
Based on the above embodiment, as preferred embodiment, the power supply unit include: solar panel and with this too
The connected built-in energy-storage battery of positive energy solar panel, the output end of the built-in energy-storage battery are set as monitoring device, edge detection
Standby and Beidou signal transceiver feeder ear.
It should be noted that MCU selects more high-end ARM core in above-mentioned edge detection equipment DSA chip architecture,
A73；AP selects the ARM core of more low side, A53；Np module uses the neural network application specific processor TPU of Google's exploitation,
Embedded TensorFlow open source software library, training, selection and the identification of video image are carried out using YOLOv3 deep learning algorithm
Equal work, 15 to 30 times faster than current GPU and CPU of TPU processing speed.Compared with traditional die, TPU is also more energy saving, function
30 to 80 times are improved in consumption efficiency (TOPS/Watt).
Referring to FIG. 5, a kind of pipeline invasion automatic early-warning system based on artificial intelligence provided by Fig. 5 the embodiment of the present application
System work operational flow diagram, system work operating procedure are as follows:
The multiple groups monitor video of monitoring camera shooting flows into, and route distribution module carries out task distribution processor, video solution
Code module carries out video decoding process, and MCU module carries out load balancing judgement, that is, judges whether the submodule m of np module is idle
(m initial value is 0) extremely should if it is not, then continuing to judge whether the submodule m+1 of np module is idle if so, AP module is loaded into video
Idle NP submodule is loaded, NP submodule runs deep learning algorithm and carries out optimal frames selection, cuts, and runs deep learning
Algorithm carries out invasion judgement (specifically, extracting the picture number of decoding video using YOLO v3 deep learning algorithm to selected frame figure
According to characteristic information；Geometry will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment
Transformation, and characteristic information is mapped to one with reference on camera；Reliable characteristic information is chosen according to the weighting of matching degree two-by-two,
And then determine position and the motion track information of intrusion target；It is detected according to the position of the intrusion target and motion track information
It whether there is intrusion behavior in the decoding video)；Intrusion behavior if it exists then generates pipeline attack early warning information and passes through north
The signal transceiver that struggles against carries out short message early warning；The video interception and video clip of memory module storage monitor video.
Remote back-office receives alarm and handles, and emergency worker invades location information according to the pipe-line equipment in warning information
It is positioned, the scene of arrival obtains all information, and judges whether there is intrusion behavior according to live all information, if existing
It carries out emergency operation and carries out pipe-line equipment maintenance, while video interception is invaded with video clip in copying equipment memory module
Behavior is called to account, and resets the warning message of source of early warning, to ensure that it continues early warning work.
It whether there is intrusion behavior in the monitor video that the application passes through detection pipe-line equipment position, believed by Beidou
Number transceiver carries out short message early warning, fast automatic early warning and can navigate to early warning position, compared to traditional artificial routine inspection mode,
Real-time is high, convenient for the implementation of emergency preplan, compared to the mode that tradition relies on vibrating sensor inspection, using more forward position
Computer vision technique, can more accurately early warning, rate of false alarm is effectively reduced.
In addition, the application reduces operation intrusion detection algorithm institute using instantly popular DSA (domain-specific) chip architecture
The power consumption needed；The application is not depended on using the publication that Beidou carries out attack early warning information using Beidou message as information carrier
Mobile communication and internet communication can solve nobody remote communication issue without 2/3/4G signal covering area, and have essence
Quasi- positioning function and short message function, can be realized the publication of warning information and the quick positioning of early warning position；The application adopts
It is solar powered with clean energy resource, meet the power demand of integral device, not only solves long distance pipeline local environment power pole
For the rare problem of inconvenient, power supply facilities, and environmentally friendly, has and sustainable use advantage.Due to the peace of integral device
Dress mode is non-intruding duct type, and cost is relatively low for installing engineering, it can be achieved that rapid deployment
Each embodiment is described in a progressive manner in specification, the highlights of each of the examples are with other realities
The difference of example is applied, the same or similar parts in each embodiment may refer to each other.For embodiment provide system and
Speech, since it is corresponding with the method that embodiment provides, so being described relatively simple, related place is referring to method part illustration
.
Specific examples are used herein to illustrate the principle and implementation manner of the present application, and above embodiments are said
It is bright to be merely used to help understand the present processes and its core concept.It should be pointed out that for the ordinary skill of the art
For personnel, under the premise of not departing from the application principle, can also to the application, some improvement and modification can also be carried out, these improvement
It is also fallen into the protection scope of the claim of this application with modification.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or apparatus that includes the element.
Claims (10)
1. a kind of pipeline based on artificial intelligence invades automatic early warning method characterized by comprising
Obtain the monitor video and location information of pipe-line equipment position；
It detects in the monitor video with the presence or absence of intrusion behavior；
If so, generating pipeline attack early warning information and carrying out short message early warning by Beidou signal transceiver.
2. pipeline according to claim 1 invades automatic early warning method, which is characterized in that the detection monitor video
In whether there is intrusion behavior, comprising:
The monitor video is subjected to task distribution processor；
Monitor video progress video decoding process is obtained into decoding video；
Judge whether the load of video processor is idle, if then loading the decoding video to video processor；
Being detected using the preset deep learning algorithm of video processor whether there is intrusion behavior in the decoding video；
If so, the video interception and video clip of storage monitor video.
3. pipeline according to claim 2 invades automatic early warning method, which is characterized in that described pre- using video processor
If deep learning algorithm detect the decoding video and whether there is intrusion behavior, comprising:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometric transformation will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment, and
Characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile letter of intrusion target
Breath；
Being detected according to the position of the intrusion target and motion track information whether there is intrusion behavior in the decoding video.
4. pipeline according to claim 1 invades automatic early warning method, which is characterized in that the generation pipeline attack early warning
Information simultaneously carries out short message early warning by Beidou signal transceiver, comprising:
The corresponding monitoring device number of the monitor video and pipe-line equipment invasion location information are generated into warning information；
Warning information to remote back-office, which is sent, by Beidou signal transceiver carries out short message early warning.
5. a kind of pipeline based on artificial intelligence invades automatic early-warning system characterized by comprising
Acquiring unit, the acquiring unit are configured to obtain the monitor video and location information of pipe-line equipment position；
Detection unit, the detection unit are configured to detect the monitor video with the presence or absence of intrusion behavior；
Prewarning unit, the prewarning unit are configured to intrusion behavior if it exists, then generate pipeline attack early warning information and pass through
Beidou signal transceiver carries out short message early warning.
6. pipeline according to claim 5 invades automatic early-warning system, which is characterized in that the detection unit is specifically wrapped
It includes:
Route distribution unit, the route distribution processing unit are configured to the monitor video carrying out task distribution processor；
Video decoding unit, the video decoding unit are configured to be solved monitor video progress video decoding process
Code video；
Load Balance Unit, the Load Balance Unit is for judging whether the load of video processor is idle, if then loading
The decoding video is to video processor；
Computing unit, the computing unit are configured to judge the decoding using the preset deep learning algorithm of video processor
It whether there is intrusion behavior in video；
Storage unit, the memory cell arrangements are used to store the video interception and piece of video of the monitor video there are intrusion behavior
Section.
7. pipeline according to claim 6 invades automatic early-warning system, which is characterized in that the computing unit is specifically used
In:
The image data characteristic information of decoding video is extracted using deep learning algorithm；
Geometric transformation will be done according to the location information of monitoring device from the image data characteristic information of different monitoring equipment, and
Characteristic information is mapped to one with reference on camera；
Reliable characteristic information is chosen according to the weighting of matching degree two-by-two, and then determines position and the motion profile letter of intrusion target
Breath；
Being detected according to the position of the intrusion target and motion track information whether there is intrusion behavior in the decoding video.
8. pipeline according to claim 5 invades automatic early-warning system, which is characterized in that the prewarning unit includes:
Generation unit, the generation unit are configured to enter the corresponding monitoring device number of the monitor video and pipe-line equipment
It invades location information and generates warning information；
Transmission unit, the transmission unit are configured to Beidou signal transceiver and send warning information to remote back-office progress
Short message early warning.
9. a kind of pipeline based on artificial intelligence invades automatic early-warning equipment characterized by comprising
Monitoring device, edge detection equipment, Beidou signal transceiver and power supply unit,
Wherein, the monitoring device is connect with edge detection equipment, for monitoring pipe-line equipment and sending monitor video in real time
To edge detection equipment；
The edge detection equipment is connect with Beidou signal transceiver, for detecting in the monitor video with the presence or absence of invasion row
For, and if it exists, it then generates pipeline attack early warning information and is sent to Beidou signal transceiver；
The Beidou signal transceiver carries out short message early warning for receiving pipeline attack early warning information and sends the position letter
Breath；
The power supply unit connect power supply with monitoring device, edge detection unit and Beidou signal transceiver.
10. pipeline according to claim 7 invades automatic early-warning equipment, which is characterized in that the monitoring device is by 4-20
A independent high definition monitoring camera composition；The edge detection equipment includes: main control unit, computing module and memory module,
The main control unit includes: routing module, Video decoding module and MCU module；The computing unit includes at AP module and NP
Manage module；The power supply unit includes: solar panel and the built-in energy-storage battery that is connected with the solar panel, described
Feeder ear of the output end of built-in energy-storage battery as monitoring device, edge detection equipment and Beidou signal transceiver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910680176.9A CN110503793A (en) | 2019-07-26 | 2019-07-26 | A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910680176.9A CN110503793A (en) | 2019-07-26 | 2019-07-26 | A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110503793A true CN110503793A (en) | 2019-11-26 |
Family
ID=68587041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910680176.9A Pending CN110503793A (en) | 2019-07-26 | 2019-07-26 | A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110503793A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507300A (en) * | 2020-04-26 | 2020-08-07 | 上海同岩土木工程科技股份有限公司 | Method for rapidly identifying piling and drilling behaviors in protected area |
| CN111815885A (en) * | 2020-07-07 | 2020-10-23 | 中南民族大学 | Intrusion early warning method, equipment, storage medium and device based on Beidou short message |
| CN113031035A (en) * | 2021-02-07 | 2021-06-25 | 北京中交创新投资发展有限公司 | Road facility data acquisition system based on artificial intelligence algorithm |
| CN114007050A (en) * | 2021-10-14 | 2022-02-01 | 桂林电子科技大学 | Target identification image transmission method based on Beidou communication |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104394381A (en) * | 2014-12-08 | 2015-03-04 | 成都星联讯科技有限公司 | Beidou short message video monitoring and pre-alarming terminal and pre-alarming data transmission method |
| CN107180504A (en) * | 2017-06-29 | 2017-09-19 | 广东电网有限责任公司佛山供电局 | A kind of the image recognition early warning system and method for the anti-big machinery destruction of transmission line of electricity |
| US20170345181A1 (en) * | 2016-05-27 | 2017-11-30 | Beijing Kuangshi Technology Co., Ltd. | Video monitoring method and video monitoring system |
| CN107888887A (en) * | 2017-12-02 | 2018-04-06 | 深圳市燃气集团股份有限公司 | A kind of video monitoring method for early warning and system for monitoring gas pipeline damage from third-party |
| CN207233141U (en) * | 2017-09-04 | 2018-04-13 | 武汉阿卡瑞思光电自控有限公司 | Oil-gas pipeline region invasion detecting device |
| CN110012268A (en) * | 2019-04-02 | 2019-07-12 | 浙江璟恩物联网科技有限公司 | Pipe network AI intelligent control method, system, readable storage medium storing program for executing and equipment |
-
2019
- 2019-07-26 CN CN201910680176.9A patent/CN110503793A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104394381A (en) * | 2014-12-08 | 2015-03-04 | 成都星联讯科技有限公司 | Beidou short message video monitoring and pre-alarming terminal and pre-alarming data transmission method |
| US20170345181A1 (en) * | 2016-05-27 | 2017-11-30 | Beijing Kuangshi Technology Co., Ltd. | Video monitoring method and video monitoring system |
| CN107180504A (en) * | 2017-06-29 | 2017-09-19 | 广东电网有限责任公司佛山供电局 | A kind of the image recognition early warning system and method for the anti-big machinery destruction of transmission line of electricity |
| CN207233141U (en) * | 2017-09-04 | 2018-04-13 | 武汉阿卡瑞思光电自控有限公司 | Oil-gas pipeline region invasion detecting device |
| CN107888887A (en) * | 2017-12-02 | 2018-04-06 | 深圳市燃气集团股份有限公司 | A kind of video monitoring method for early warning and system for monitoring gas pipeline damage from third-party |
| CN110012268A (en) * | 2019-04-02 | 2019-07-12 | 浙江璟恩物联网科技有限公司 | Pipe network AI intelligent control method, system, readable storage medium storing program for executing and equipment |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507300A (en) * | 2020-04-26 | 2020-08-07 | 上海同岩土木工程科技股份有限公司 | Method for rapidly identifying piling and drilling behaviors in protected area |
| CN111815885A (en) * | 2020-07-07 | 2020-10-23 | 中南民族大学 | Intrusion early warning method, equipment, storage medium and device based on Beidou short message |
| CN113031035A (en) * | 2021-02-07 | 2021-06-25 | 北京中交创新投资发展有限公司 | Road facility data acquisition system based on artificial intelligence algorithm |
| CN113031035B (en) * | 2021-02-07 | 2023-07-21 | 交信北斗科技有限公司 | Road facility data acquisition system based on artificial intelligence algorithm |
| CN114007050A (en) * | 2021-10-14 | 2022-02-01 | 桂林电子科技大学 | Target identification image transmission method based on Beidou communication |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110503793A (en) | A kind of pipeline invasion automatic early warning method, system and equipment based on artificial intelligence | |
| CN111536429B (en) | Decision fusion-based oil and gas pipeline early warning system and method | |
| JP3543426B2 (en) | Pipe network management method and system | |
| CN110490121A (en) | A kind of transmission line of electricity external force damage prevention method and system for clapping image recognition based on more prisons | |
| CN107888887A (en) | A kind of video monitoring method for early warning and system for monitoring gas pipeline damage from third-party | |
| CN106600887A (en) | Video monitoring linkage system based on substation patrol robot and method thereof | |
| CN104125010A (en) | Optical cable fault location method and device thereof | |
| CN103927834B (en) | Underground utilities circumference invades and harasses early warning system | |
| CN107765854A (en) | A kind of polling transmission line method based on augmented reality | |
| CN103985008A (en) | Intelligent monitoring system of city underground pipeline | |
| CN102635786A (en) | Online real-time monitoring system for an embedded delivery pipeline | |
| CN110779568A (en) | Power cable detection method and device with cooperation of online monitoring and mobile inspection | |
| CN104243192B (en) | Fault handling method and system | |
| CN107612503A (en) | Solar panel Fault detection method and system and robot | |
| CN103899357A (en) | System and method for real-time visual monitoring and early warning of mine floor water disasters | |
| CN109253400A (en) | Pipe vibration method for sensing and system based on distributing optical fiber sensing | |
| CN112684744A (en) | Wisdom city application underground pipe gallery model monitoring and early warning system | |
| CN108731682A (en) | A kind of path planning system and method applied to underground mine rescue | |
| CN103871185B (en) | The anti-external force processing method of transmission line of electricity, Apparatus and system | |
| CN103763137B (en) | A kind of device configuration connection guard method, system and device | |
| KR20110093245A (en) | A method to manage the district heating facility | |
| CN110989410A (en) | Data management system and method for flexible pipeline robot | |
| CN110597115A (en) | Cable head real-time monitoring device and data processing communication method thereof | |
| CN201129623Y (en) | Oil gas pipe radar safe early-warning system | |
| CN213069912U (en) | Inspection system for urban drainage tunnel |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191126 |
|
| RJ01 | Rejection of invention patent application after publication |