CN111274880A - Video intelligent analysis auxiliary inspection and abnormity warning method - Google Patents
Video intelligent analysis auxiliary inspection and abnormity warning method Download PDFInfo
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- CN111274880A CN111274880A CN202010026363.8A CN202010026363A CN111274880A CN 111274880 A CN111274880 A CN 111274880A CN 202010026363 A CN202010026363 A CN 202010026363A CN 111274880 A CN111274880 A CN 111274880A
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000007689 inspection Methods 0.000 title claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 15
- 238000012549 training Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 23
- 238000012550 audit Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 claims 5
- 238000012216 screening Methods 0.000 abstract description 7
- 238000001914 filtration Methods 0.000 abstract description 3
- 238000002372 labelling Methods 0.000 abstract 1
- 238000013024 troubleshooting Methods 0.000 abstract 1
- 230000005540 biological transmission Effects 0.000 description 11
- 238000012423 maintenance Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 208000025274 Lightning injury Diseases 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012212 insulator Substances 0.000 description 1
- 238000004643 material aging Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000036319 strand breaking Effects 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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Abstract
The invention provides a video intelligent analysis auxiliary inspection and abnormity warning method, which comprises the following steps of classifying video image data, and carrying out abnormity labeling on images in a video image library based on an image description algorithm; constructing a training set by the generated basic alarm record data, and constructing a learning model for alarm convergence; setting an alarm threshold index, and carrying out classification output on video image data of a video image library, wherein the classification comprises effective faults, faults to be detected and no faults. The invention can give an alarm in time when an abnormal accident occurs, is convenient to screen in time, has legal evidence and documented, and can be used for system research of equipment state polling and screening methods, thereby setting an alarm threshold value index, setting corresponding upper and lower limits according to the alarm type and the alarm level, selectively troubleshooting the accident and reporting the alarm event according to the filtering condition, reducing false alarms and missing alarms, and greatly improving the reliability of the system.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a video intelligent analysis auxiliary inspection and abnormal alarm method.
Background
At present, transmission lines in China still mainly adopt overhead lines, high-voltage and extra-high-voltage transmission lines are distributed at multiple points and are wide in range, the landforms of the transmission lines are complex, the natural environments are severe, and in mountain areas, due to water and soil loss, rainwater, debris flow and the like, the foundation of the transmission lines, particularly power facilities such as towers and the like can be damaged, the towers topple or equipment is damaged, and the transmission lines are directly damaged. The power line and the pole tower accessory are exposed outdoors for a long time and are influenced by factors such as continuous mechanical tension, lightning flashover, material aging, human factors and the like to cause damages such as tower falling, strand breaking, abrasion, corrosion, stress and the like, the insulator is also damaged by lightning stroke, the power transmission line is discharged due to the growth of trees, and the pole tower is stolen and other accidents. Therefore, it is necessary to detect the transmission line in time to ensure the safe operation of the transmission line. The traditional manual inspection mode has large workload and hard conditions, and particularly for the inspection of the power transmission lines in mountainous areas and across large rivers in ice disasters, flood disasters, earthquakes, landslides and nights, the inspection takes long time, and has high labor cost, great difficulty and high risk.
In the traditional inspection of the power transmission line, the equipment such as a high-power telescope, a thermal infrared imager and the like is carried by the naked eyes or the manpower to search the fault point according to the approximate range given by fault distance measurement and the tower-by-tower gear-by-gear line-by-field environment. The fault point with obvious discharge trace on the ground or the power transmission line equipment is easily found by manual work, but the fault point is hardly found by manual work through a high power telescope on the ground or has the condition of visual range or visual angle limitation, and the fault point is searched by manually stepping on a tower or even routing with electricity, so that the workload of line operation and maintenance personnel is greatly increased, and the safety threat is brought to live working personnel.
Therefore, it is highly desirable for those skilled in the art to provide a method for video intelligent analysis assisted inspection and abnormal alarm, which utilizes a diversified active alarm technique to realize accurate pre-judgment and fault alarm of abnormal state of equipment.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for assisting the routing inspection and the abnormal alarming by video intelligent analysis is provided, and the accurate prejudgment and the fault alarming of the abnormal state of the equipment are realized by utilizing a diversified active alarming technology.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for assisting in routing inspection and abnormal alarming in video intelligent analysis comprises the following steps:
s01, acquiring video image data through unmanned aerial vehicle oblique photography, and forming a video image library;
step S02, video image data are classified, semantic annotation is carried out on the classified video images, and an annotation set is formed by centralizing the semantic annotation;
s03, extracting basic features from a video image library by using a convolutional neural network based on an image description algorithm, and carrying out abnormal annotation on images in the video image library;
step S04, constructing a training set by the generated basic alarm record data, and constructing a learning model for alarm convergence;
step S05, setting an alarm threshold index, and classifying and outputting video image data of a video image library, wherein the classification comprises effective faults, faults to be detected and no faults;
and step S06, judging whether to alarm or not, selecting an alarm mode and an alarm threshold, if the fault is valid, executing alarm processing, if the fault to be detected is pushed to be checked manually, marking or deleting operation, and if the fault is not detected, no alarm is needed.
Preferably, the classification output in step S05 establishes a training sample for machine learning to continuously optimize and adjust the recognition rule, so as to achieve the purposes of intelligent alarm convergence and reduction of false alarm probability.
Preferably, the step S06 further includes extracting feature information of the record for executing the alarm processing, and matching the extracted feature information to the record to be detected.
Preferably, if the same characteristic information exists, the fault to be detected is divided into effective faults; and if the same characteristic information does not exist, pushing manual audit to the fault to be detected.
Preferably, the records for performing the alarm processing are stored in a storage device and the records for performing the alarm processing are classified into a light level, a medium level, and a heavy level.
Preferably, when the record of performing the alarm processing is the heavy level, the record of performing the alarm processing is transmitted to the mobile terminal in real time.
Preferably, the classification threshold value of the record for executing the alarm processing is set, and when the record for executing the alarm processing is at the medium level and the heavy level, the record for executing the alarm processing is sent to the mobile terminal in real time.
The invention provides a video intelligent analysis auxiliary polling and abnormity warning method, which can timely warn when an abnormal accident occurs, timely and convenient screening, legal and well-documented equipment state polling and screening methods are researched by a system, a warning threshold value index is formulated, and corresponding upper and lower limits are formulated according to the warning type and the warning level. The user can set the threshold values in a self-defined mode, the system can selectively troubleshoot accidents and report alarm events according to the filtering conditions during polling, false alarms are reduced, meanwhile, missed alarms are reduced, and therefore the reliability of the system can be greatly improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a video intelligent analysis aided inspection and anomaly alarm method of the present invention.
Detailed Description
In order to make the contents of the present invention more comprehensible, the present invention is further described below with reference to the accompanying drawings. The invention is of course not limited to this particular embodiment, and general alternatives known to those skilled in the art are also covered by the scope of the invention. The present invention is described in detail with reference to the drawings, and the drawings are not to be considered as limiting the invention, but are enlarged partially in accordance with the general scale for convenience of explanation.
The invention provides a video intelligent analysis auxiliary inspection and abnormal alarm method, which comprises the following steps:
s01, acquiring video image data through unmanned aerial vehicle oblique photography, and forming a video image library;
step S02, video image data are classified, semantic annotation is carried out on the classified video images, and an annotation set is formed by centralizing the semantic annotation;
s03, extracting basic features from a video image library by using a convolutional neural network based on an image description algorithm, and carrying out abnormal annotation on images in the video image library;
step S04, constructing a training set by the generated basic alarm record data, and constructing a learning model for alarm convergence;
step S05, setting an alarm threshold index, and classifying and outputting video image data of a video image library, wherein the classification comprises effective faults, faults to be detected and no faults; the classification output in the step S05 establishes training samples for machine learning to continuously optimize and adjust the recognition rules, so as to achieve the purposes of intelligent alarm convergence and reduction of false alarm probability.
And step S06, judging whether to alarm or not, selecting an alarm mode and an alarm threshold, if the fault is valid, executing alarm processing, if the fault to be detected is pushed to be checked manually, marking or deleting operation, and if the fault is not detected, no alarm is needed.
Step S06 also includes extracting the characteristic information of the record of executing the alarm processing, and matching the extracted characteristic information to the record of the fault to be detected; if the same characteristic information exists, dividing the fault to be detected into effective faults; and if the same characteristic information does not exist, pushing manual audit to the fault to be detected.
In addition, records for executing the alarm processing are stored in the storage device, and the records for executing the alarm processing are classified, wherein the records comprise a light level, a medium level and a heavy level; and when the record for executing the alarm processing is the heavy level, the record for executing the alarm processing is sent to the mobile terminal in real time. And setting a classification threshold value for the record for executing the alarm processing, and sending the record for executing the alarm processing to the mobile terminal in real time when the record for executing the alarm processing is in a medium level and a heavy level.
In order to enable the system to give an alarm in time when an abnormal accident occurs, to facilitate screening in time, to make legal and documented, to study a device state polling and screening method, an alarm threshold value index is formulated, and corresponding upper and lower limits are formulated according to an alarm type and an alarm level. The user can customize these thresholds and the system can selectively troubleshoot incidents and report alarm events based on these filter conditions when polling. Meanwhile, the system can also perform alarm and false alarm processing of the power grid equipment, system operation and maintenance and false alarm processing, and processing as an emergency if false alarm is generated. False alarm is reduced, and missing alarm is also reduced, so that the reliability of the system can be greatly improved.
The invention provides a video intelligent analysis auxiliary polling and abnormity warning method, which can timely warn when an abnormal accident occurs, timely and convenient screening, legal and well-documented equipment state polling and screening methods are researched by a system, a warning threshold value index is formulated, and corresponding upper and lower limits are formulated according to the warning type and the warning level. The user can set the threshold values in a self-defined mode, the system can selectively troubleshoot accidents and report alarm events according to the filtering conditions during polling, false alarms are reduced, meanwhile, missed alarms are reduced, and therefore the reliability of the system can be greatly improved.
Although the present invention has been described mainly in the above embodiments, it is described as an example only and the present invention is not limited thereto. Numerous modifications and applications will occur to those skilled in the art without departing from the essential characteristics of the embodiments. For example, each of the components detailed for the embodiments may be modified and operated, and the differences associated with the variants and applications may be considered to be included within the scope of protection of the invention as defined by the following claims.
Reference in the specification to an embodiment means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with any embodiment, it is submitted that it is within the purview of one skilled in the art to effect such feature, structure, or characteristic in connection with other ones of the embodiments.
Claims (7)
1. A video intelligent analysis auxiliary inspection and abnormal alarm method is characterized by comprising the following steps:
s01, acquiring video image data through unmanned aerial vehicle oblique photography, and forming a video image library;
step S02, video image data are classified, semantic annotation is carried out on the classified video images, and an annotation set is formed by centralizing the semantic annotation;
s03, extracting basic features from a video image library by using a convolutional neural network based on an image description algorithm, and carrying out abnormal annotation on images in the video image library;
step S04, constructing a training set by the generated basic alarm record data, and constructing a learning model for alarm convergence;
step S05, setting an alarm threshold index, and classifying and outputting video image data of a video image library, wherein the classification comprises effective faults, faults to be detected and no faults;
and step S06, judging whether to alarm or not, selecting an alarm mode and an alarm threshold, if the fault is valid, executing alarm processing, if the fault to be detected is pushed to be checked manually, marking or deleting operation, and if the fault is not detected, no alarm is needed.
2. The video intelligent analysis aided inspection and abnormality alarming method according to claim 1, wherein the classification output in the step S05 establishes training samples for machine learning to continuously optimize and adjust recognition rules so as to achieve the purposes of intelligent alarm convergence and reduction of false alarm probability.
3. The video intelligent analysis assisted inspection and abnormality warning method according to claim 1, wherein the step S06 further includes extracting feature information of the record for performing the warning process, and matching the extracted feature information to the record for the fault to be detected.
4. The video intelligent analysis auxiliary inspection and abnormality warning method according to claim 3, wherein if the same characteristic information exists, the fault to be detected is classified as an effective fault; and if the same characteristic information does not exist, pushing manual audit to the fault to be detected.
5. The video intelligent analysis assisted inspection and anomaly alarm method according to claim 3, wherein records for performing alarm processing are stored in a storage device and classified, including a light level, a medium level, and a heavy level.
6. The video intelligent analysis assisted inspection and abnormality warning method according to claim 5, wherein when the record of performing the warning process is a heavy level, the record of performing the warning process is transmitted to the mobile terminal in real time.
7. The video intelligent analysis aided inspection and abnormality warning method according to claim 5, wherein a classification threshold is set for a record of performing the warning process, and when the record of performing the warning process is at a middle level or a heavy level, the record of performing the warning process is transmitted to the mobile terminal in real time.
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Cited By (5)
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| CN112132819A (en) * | 2020-09-29 | 2020-12-25 | 国网上海市电力公司 | Communication network management monitoring method based on artificial intelligence |
| CN112288711A (en) * | 2020-10-28 | 2021-01-29 | 浙江华云清洁能源有限公司 | Unmanned aerial vehicle inspection image defect image identification method, device, equipment and medium |
| CN112541455A (en) * | 2020-12-21 | 2021-03-23 | 国网河南省电力公司电力科学研究院 | Machine vision-based method for predicting accident of pole breakage of concrete pole of distribution network |
| CN113823012A (en) * | 2021-09-13 | 2021-12-21 | 浙江众合科技股份有限公司 | BIM technology-based rail transit facility inspection method and device |
| CN117478843A (en) * | 2023-11-15 | 2024-01-30 | 廊坊博联科技发展有限公司 | Intelligent patrol control system and method |
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Application publication date: 20200612 |