CN114219194A - Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification - Google Patents
Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification Download PDFInfo
- Publication number
- CN114219194A CN114219194A CN202111001251.8A CN202111001251A CN114219194A CN 114219194 A CN114219194 A CN 114219194A CN 202111001251 A CN202111001251 A CN 202111001251A CN 114219194 A CN114219194 A CN 114219194A
- Authority
- CN
- China
- Prior art keywords
- risk
- transmission line
- power transmission
- virtual
- target object
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
Abstract
The invention provides a power transmission line risk warning method and device based on front-end AI identification, wherein the method comprises the following steps: establishing a virtual risk area of the power transmission line by taking any power transmission line as a center, setting horizontal safety thresholds of a first virtual fence, a second virtual fence and a risk object, and setting a risk height threshold H of the first virtual fenceset(ii) a Acquiring a moving image of a target object entering a virtual risk area of the power transmission line through an image acquisition module, and calculating the direct height H of the target object through a binocular image ranging method; comparing the direct altitude H to the risk altitude threshold HsetIf yes, alarming; if not, identifying the moving image, and further judging whether the target object is an extensible vehicle; if yes, judging the target objectAnd if the clear distance crossing risk or construction operation risk condition after entering the first virtual fence exists, the mobile image is sent to a background monitoring center and an alarm is given.
Description
Technical Field
The invention relates to the technical field of power transmission equipment safety, in particular to a power transmission line risk warning method and device based on front-end AI identification.
Background
Overhead transmission lines span sections of high speed railways, highways and important transmission channels, referred to as "triple spans". In order to prevent accidents such as tower collapse, disconnection, string drop and the like of the three-span section and prevent larger public safety and power grid safety events caused by insufficient clear distance of cross span, image/video monitoring needs to be carried out on the three-span section. In addition, around the power transmission corridor, operations such as various mine developments, building construction, road construction and the like are more and more, image/video monitoring is needed, construction operation risk hazards are identified through images, and warning measures are taken.
The existing transmission channel image monitoring technology is mostly installed on a transmission tower, only has image acquisition and image transmission functions, and is carried out in a power grid monitoring center at the rear end for processing and identifying hidden dangers of monitoring images. The image recognition at the rear end basically achieves the detection and recognition of moving objects, but lacks the functions of object height recognition, risk discrimination and the like, so that a large number of risk-free recognition results are reported, and interference is caused to the disposal decision of power grid operation and maintenance personnel.
Although the electronic fence technology based on ultrasonic ranging is also used for safety precaution of a power transmission channel and a transformer substation, the measured distance is only the specific distance from a moving object to the installation point of the monitoring device, and has far difference with the horizontal and vertical distance requirements of a power transmission line phase line and a crossed object specified in the overhead power transmission line operating regulations, and the measured distance does not have due effect.
Therefore, a terminal for front-end image monitoring is developed, which can perform moving object type identification and object height and length ranging by using AI image identification, has functions of front-end risk alarm and the like, and has important engineering value for monitoring the risk potential of a three-span section and a construction operation area.
Disclosure of Invention
The invention aims to provide a power transmission line risk warning method based on front-end AI identification, so as to solve the problems in the background technology.
The invention is realized by the following technical scheme: the invention discloses a power transmission line risk warning method based on front-end AI identification, which comprises the following steps:
establishing a virtual risk area of the power transmission line by taking any power transmission line as a center, setting horizontal safety thresholds of the first virtual fence, the second virtual fence and a risk object and setting a risk height threshold H of the first virtual fenceset；
Acquiring a moving image of a target object entering a virtual risk area of the power transmission line through an image acquisition module, and calculating the direct height H of the target object through a binocular image ranging method;
comparing the direct altitude H to the risk altitude threshold HsetIf the direct height H exceeds the risk height threshold HsetSending the moving image to a background monitoring center, and alarming;
if the direct height H does not exceed the risk height threshold HsetRecognizing the moving image, and judging whether the target object is an extensible vehicle;
if yes, the clear distance out-of-limit risk or construction operation risk condition after the target object enters the first virtual fence is judged, and if the risk exists, the moving image is sent to a background monitoring center and an alarm is given.
Optionally, any power transmission line is used as a central axis, a first virtual risk area is established, the boundary of the first virtual risk area is a first virtual fence, and a first horizontal safety threshold D existing between the power transmission line and the first virtual fence is set1Setting a risk height threshold H of the first virtual fenceset；
Respectively establishing second virtual risk areas on two sides of the first virtual risk area, setting the boundary of the second virtual risk area as a second virtual fence, and setting a second horizontal safety threshold value D between the first virtual fence and the second virtual fence2Said second level safety threshold D2Greater than the first level safety threshold D2。
Optionally, calculating the direct height H of the target object by using a binocular image ranging method includes: preprocessing the shot moving image;
forming parallax values of images at multiple shooting moments by utilizing the moving images shot at different angles at the same moment, and carrying out target edge detection in a chain code mode to obtain the highest point edge pixel coordinate of a target object in the vertical direction;
and (4) combining the binocular imaging three-dimensional coordinate conversion relationship to obtain the space point three-dimensional coordinates of each detected pixel coordinate, and obtaining the direct height H of the target object.
Optionally, recognizing the moving image, and determining whether the target object is an extendable vehicle, includes:
establishing an image database, wherein the image database comprises human body images, animal images and vehicle images, and the vehicle images comprise a plurality of stretchable vehicle images;
and comparing the moving image with the content in the image database, and judging whether the target object is an extensible vehicle.
Optionally, the determining the clear distance out-of-limit risk condition after the target object enters the first virtual fence includes:
if the target object is identified as an extendable vehicle, an extension height H of the extendable vehicle is determined according to the type of the extendable vehicle2Determining the extension height H2Whether the risk height threshold H is exceededsetIf the net distance exceeds the net distance threshold, the net distance is out of limit.
Optionally, the determining the risk of the construction operation after the target object enters the first virtual fence includes:
calculating the horizontal distance D between the power transmission line and the target object by a binocular ranging method, and judging whether the horizontal distance D is smaller than a first horizontal safety threshold D1If yes, the construction operation risk exists.
The invention discloses a transmission line risk image warning device, which is used for implementing the transmission line risk warning method based on front-end AI identification in the first aspect of the invention and comprises a transmission line monitoring terminal, wherein the transmission line monitoring terminal comprises a power module, a central processing module, an image acquisition module, a storage module, a communication module and an audible and visual alarm;
the power supply module is used for supplying power to the monitoring terminal;
the central processing module adopts a chip with AI image recognition to realize front-end image processing and various control instructions;
the image acquisition module is used for acquiring images and videos so that the acquired images have distance measurement conditions;
the storage module is used for storing an image library of moving objects such as people, animals and vehicles for image recognition and images and other monitoring and risk recognition data acquired and shot by the image acquisition module;
the communication module is used for monitoring communication between the terminal and the background monitoring center;
and the audible and visual alarm sends out flashing warning lights and risk warning voice according to the received risk warning instruction.
Compared with the prior art, the invention has the following beneficial effects:
according to the power transmission line risk warning method and device based on front-end AI identification, the risk source is identified through the front-end AI image, so that the communication pressure is reduced, and the timeliness and the accuracy of risk judgment are improved; setting horizontal and vertical electronic fences according to the operation regulations of the overhead transmission line, judging the risks of construction vehicles such as cranes, excavators and bulldozers entering the electronic fences, avoiding blind identification of moving objects, and sending information of non-height out-of-limit vehicles and non-construction operation vehicles to power grid operation and maintenance personnel to cause operation and maintenance disposal decision interference; the binocular camera can realize the image ranging function of the moving object, so that the clear distance out-of-limit risk judgment is more accurate and reliable; and the audible and visual alarm respectively sends out pointed voice warning contents for different identified risk types, so that a driver on site takes pointed risk reduction measures, and the risk treatment effect is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred 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 inventive exercise.
Fig. 1 is a flowchart of a power transmission line risk warning method based on front-end AI identification according to the present invention;
fig. 2 is another flowchart of a power transmission line risk warning method based on front-end AI identification according to the present invention;
fig. 3 is an exemplary diagram of a power transmission line provided by the present invention, in which a first virtual risk area and a second virtual risk area are set;
fig. 4 is a schematic diagram of main modules of the power transmission line monitoring terminal provided by the invention.
In the figure, 1 is a power module, 2 is a central processing module, 3 is an image acquisition module, 4 is a storage module, 5 is a communication module, and 6 is an audible and visual alarm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of embodiments of the invention and not all embodiments of the invention, with the understanding that the invention is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the invention described herein without inventive step, shall fall within the scope of protection of the invention.
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
It is to be understood that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.
In order to provide a thorough understanding of the present invention, a detailed structure will be set forth in the following description in order to explain the present invention. Alternative embodiments of the invention are described in detail below, however, the invention may be practiced in other embodiments that depart from these specific details.
Referring to fig. 1 to 4, a first aspect of the present invention discloses a power transmission line risk warning method based on front-end AI identification, including the following steps:
s1, establishing a virtual risk area of the power transmission line by taking any power transmission line as a center, wherein the virtual risk area of the power transmission lineThe boundary of the risk area comprises a first virtual fence and a second virtual fence, the horizontal safety thresholds of the first virtual fence, the second virtual fence and the risk object are set, and the risk height threshold H of the first virtual fence is setset。
In step S1, a first virtual risk area is established with any one of the power transmission lines as a central axis according to the regulations on cross over in GB 50545 "110 kV-750 kV overhead power transmission line design specification" and DL/T741 "overhead power transmission line operating specification", a boundary of the first virtual risk area is a first virtual fence, and a first horizontal safety threshold D existing between the power transmission line and the first virtual fence is set1First level safety threshold D1Setting according to the horizontal safety distance limit value of the power transmission line and the crossed object, indicating that the object entering the area does not meet the horizontal safety distance of the power transmission line, and simultaneously setting the risk height threshold value H of the first virtual fencesetRisk height threshold HsetThe vertical safe distance limit value and the horizontal safe distance limit value are set according to the vertical safe distance limit value of the power transmission line and the spanned object, and the specific values of the vertical safe distance limit value and the horizontal safe distance limit value are common knowledge of persons skilled in the art, and the embodiment is not specifically explained herein.
Respectively establishing second virtual risk areas on two sides of the first virtual risk area, setting the boundary of the second virtual risk area as a second virtual fence, and setting a second horizontal safety threshold value D between the first virtual fence and the second virtual fence2Second level safety threshold D2At said first level a safety threshold D1The distance of 5-10m is increased on the basis.
S2, acquiring a moving image of the target object entering the virtual risk area of the power transmission line through an image acquisition module, and calculating the direct height H of the target object through a binocular image ranging method;
alternatively, in step S2, the direct height H of the target object is calculated by: preprocessing the shot moving images and correcting polar lines, then forming parallax values of images at multiple shooting moments by using the moving images shot at different angles at the same moment, and carrying out target edge detection in a chain code mode to obtain the highest point edge pixel coordinate of a target object in the vertical direction; and finally, combining a binocular imaging three-dimensional coordinate conversion relation to obtain the space point three-dimensional coordinates of each detected pixel coordinate, and obtaining the direct height H of the target object.
S3, comparing the direct altitude H with the risk altitude threshold HsetIf the direct height H exceeds the risk height threshold HsetAnd sending the moving image to a background monitoring center and giving an alarm.
Note that the risk height threshold HsetIn addition to being empirically set, the calculation can be performed by the following formula: hset＝Htower-HSag-Hmargin；
Wherein HtowerThe height H of the tower position where the lowest phase of the phase line of the transmission line is positioned is shownsagThe representation represents the maximum sag, H, of the transmission linemarginIndicating the insulation distance margin.
S4, if the direct height H does not exceed the risk height threshold HsetRecognizing the moving image to determine whether the target object is an extensible vehicle, wherein recognizing the moving image to determine whether the target object is an extensible vehicle includes: establishing an image database, wherein the image database comprises human body images, animal images and vehicle images, and the vehicle images comprise a plurality of stretchable vehicle images; (ii) a
And comparing the moving image with the content in the image database, and judging whether the target object is an extensible vehicle.
And S5, if so, judging the net distance out-of-limit risk or construction operation risk condition of the target object after entering the first virtual fence, wherein the net distance out-of-limit risk represents whether the extending height of the extensible vehicle exceeds a risk height threshold value, and the construction operation risk refers to whether the distance from the vehicle to the tower during construction operation is smaller than a first horizontal safety threshold value.
Wherein judge the clear distance after the target object gets into first virtual rail and cross limit the risk condition, include:
if the target object is identified as an extendable vehicle, an extension height H of the extendable vehicle is determined according to the type of the extendable vehicle2Determining the extension height H2Whether the risk height threshold H is exceededsetIf the net distance exceeds the net distance threshold, the net distance is out of limit.
Further, the determining the risk of the construction operation after the target object enters the first virtual fence includes:
calculating the horizontal distance D between the power transmission line and the target object, and calculating the horizontal distance D of the target object in the following way: preprocessing the shot moving images and correcting polar lines, then forming parallax values of images at multiple shooting moments by using the moving images shot at different angles at the same moment, and carrying out target edge detection in a chain code mode to obtain the highest point edge pixel coordinate of a target object in the vertical direction; and finally, acquiring the three-dimensional coordinates of the space points of the detected pixel coordinates by combining the binocular imaging three-dimensional coordinate conversion relation to obtain the horizontal distance D of the target object, judging whether the horizontal distance D is smaller than a first horizontal safety threshold D1, and if so, indicating that the construction operation risk exists.
It should be noted that when the target object enters the second virtual risk area enclosed by the second virtual fence, the target object is marked as a key attention object, but no alarm is given.
The invention discloses a transmission line risk image warning device, which is used for implementing the transmission line risk warning method based on front-end AI identification in the first aspect of the invention and comprises a transmission line monitoring terminal, wherein the transmission line monitoring terminal can be arranged on a transmission tower and supplies power to a power module through a solar panel; the power transmission line monitoring terminal can also be arranged on a wire, and adopts wire induction power supply to supply power to the power supply module 1, and comprises the power supply module 1, a central processing module 2, an image acquisition module 3, a storage module 4, a communication module 5 and an audible and visual alarm 6;
the power supply module 1 is used for supplying power to the monitoring terminal;
the central processing module 2 adopts a chip with AI image recognition to realize front-end image processing and various control instructions;
the image acquisition module 3 is used for acquiring images and videos so that the acquired images have distance measurement conditions;
the storage module 4 is used for storing an image library of moving objects such as people, animals and vehicles for image recognition, images acquired and shot by the image acquisition module 3 and other monitoring and risk recognition data;
the communication module 5 is used for monitoring communication between the terminal and the background monitoring center;
and the audible and visual alarm 6 sends out flashing warning lights and risk warning voice according to the received risk warning instruction.
It is to be noted that, when H > HsetIn the meantime, the voice warning content of the audible and visual alarm 6 is ' the height of the vehicle is out of limit and the high-voltage electric shock danger ' is generated ';
when H is present2＞HsetWhen the user wants to use the device, the voice warning content is' please pay attention to the traffic to prevent high-voltage electric shock! "; when D is less than D1In the meantime, the voice warning content is "please proceed construction cautiously, prevent the tower footing from being destroyed! ".
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A transmission line risk warning method based on front-end AI identification is characterized by comprising the following steps:
establishing a virtual risk area of the power transmission line by taking any power transmission line as a center, setting horizontal safety thresholds of the first virtual fence, the second virtual fence and a risk object and setting a risk height threshold H of the first virtual fenceset；
Acquiring a moving image of a target object entering a virtual risk area of the power transmission line through an image acquisition module, and calculating the direct height H of the target object through a binocular image ranging method;
comparing the direct altitude H to the risk altitude threshold HsetIf the direct height H exceeds the risk height threshold HsetSending the moving image to a background monitoring center, and alarming;
if the direct height H does not exceed the risk height threshold HsetRecognizing the moving image, and judging whether the target object is an extensible vehicle;
if yes, the clear distance out-of-limit risk or construction operation risk condition after the target object enters the first virtual fence is judged, and if the risk exists, the moving image is sent to a background monitoring center and an alarm is given.
2. The power transmission line risk warning method based on front-end AI identification as recited in claim 1, wherein any power transmission line is used as a central axis to establish a first virtual risk area, the boundary of the first virtual risk area is a first virtual fence, and a first horizontal safety threshold D existing between the power transmission line and the first virtual fence is set1Setting a risk height threshold H of the first virtual fenceset；
Respectively establishing second virtual risk areas on two sides of the first virtual risk area, setting the boundary of the second virtual risk area as a second virtual fence, and setting a second horizontal safety threshold value D between the first virtual fence and the second virtual fence2Said second level safety threshold D2Greater than the first level safety threshold D2。
3. The power transmission line risk warning method based on front-end AI identification as claimed in claim 1, wherein calculating the direct height H of the target object by binocular image ranging comprises: preprocessing the shot moving image;
forming parallax values of images at multiple shooting moments by utilizing the moving images shot at different angles at the same moment, and carrying out target edge detection in a chain code mode to obtain the highest point edge pixel coordinate of a target object in the vertical direction;
and (4) combining the binocular imaging three-dimensional coordinate conversion relationship to obtain the space point three-dimensional coordinates of each detected pixel coordinate, and obtaining the direct height H of the target object.
4. The power transmission line risk warning method based on front-end AI identification as claimed in claim 1 wherein identifying the moving images to determine if a target object is an extendable vehicle comprises:
establishing an image database, wherein the image database comprises human body images, animal images and vehicle images, and the vehicle images comprise a plurality of stretchable vehicle images;
and comparing the moving image with the content in the image database, and judging whether the target object is an extensible vehicle.
5. The power transmission line risk warning method based on front-end AI identification as recited in claim 1 wherein determining a clear distance out-of-limit risk condition after a target object enters the first virtual fence comprises:
if the target object is identified as an extendable vehicle, an extension height H of the extendable vehicle is determined according to the type of the extendable vehicle2Determining the extension height H2Whether the risk height threshold H is exceededsetIf the net distance exceeds the net distance threshold, the net distance is out of limit.
6. The power transmission line risk warning method based on front-end AI identification as recited in claim 1 wherein determining a risk of a construction operation after a target object enters the first virtual fence comprises:
calculating the horizontal distance D between the power transmission line and the target object by a binocular ranging method, and judging whether the horizontal distance D is smaller than a first horizontal safety threshold D1If so, thenIndicating that there is a risk of construction work.
7. A transmission line risk image warning device is used for implementing the transmission line risk warning method based on front-end AI identification according to any one of claims 1 to 6, and is characterized by comprising a transmission line monitoring terminal, wherein the transmission line monitoring terminal comprises a power supply module, a central processing module, an image acquisition module, a storage module, a communication module and an audible and visual alarm;
the power supply module is used for supplying power to the monitoring terminal;
the central processing module adopts a chip with AI image recognition to realize front-end image processing and various control instructions;
the image acquisition module is used for acquiring images and videos so that the acquired images have distance measurement conditions;
the storage module is used for storing an image library of moving objects such as people, animals and vehicles for image recognition and images and other monitoring and risk recognition data acquired and shot by the image acquisition module;
the communication module is used for monitoring communication between the terminal and the background monitoring center;
and the audible and visual alarm sends out flashing warning lights and risk warning voice according to the received risk warning instruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111001251.8A CN114219194A (en) | 2021-08-30 | 2021-08-30 | Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111001251.8A CN114219194A (en) | 2021-08-30 | 2021-08-30 | Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114219194A true CN114219194A (en) | 2022-03-22 |
Family
ID=80695917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111001251.8A Pending CN114219194A (en) | 2021-08-30 | 2021-08-30 | Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114219194A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052223A (en) * | 2023-04-03 | 2023-05-02 | 浪潮通用软件有限公司 | Method, system, equipment and medium for identifying people in operation area based on machine vision |
| CN116597390A (en) * | 2023-07-18 | 2023-08-15 | 南方电网数字电网研究院有限公司 | Method and device for detecting construction hidden danger around power transmission line and computer equipment |
| CN117789131A (en) * | 2024-02-18 | 2024-03-29 | 广东电网有限责任公司广州供电局 | Risk monitoring method, risk monitoring device, risk monitoring equipment and storage medium |
-
2021
- 2021-08-30 CN CN202111001251.8A patent/CN114219194A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052223A (en) * | 2023-04-03 | 2023-05-02 | 浪潮通用软件有限公司 | Method, system, equipment and medium for identifying people in operation area based on machine vision |
| CN116597390A (en) * | 2023-07-18 | 2023-08-15 | 南方电网数字电网研究院有限公司 | Method and device for detecting construction hidden danger around power transmission line and computer equipment |
| CN116597390B (en) * | 2023-07-18 | 2023-12-12 | 南方电网数字电网研究院有限公司 | Method and device for detecting construction hidden danger around power transmission line and computer equipment |
| CN117789131A (en) * | 2024-02-18 | 2024-03-29 | 广东电网有限责任公司广州供电局 | Risk monitoring method, risk monitoring device, risk monitoring equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114219194A (en) | Power transmission line risk warning method and device based on front-end AI (Artificial Intelligence) identification | |
| CN107328465B (en) | Submarine cable vibration monitoring system | |
| CN110255380B (en) | Crane operation method and device | |
| CN109284739A (en) | A kind of preventing damage to power transmission line caused by external force method for early warning and system based on deep learning | |
| CN103287464B (en) | Intelligent video surveillance system at railway crossing and implement method of system | |
| CN208722367U (en) | Multi-level transmission line of electricity solid external force damage prevention device | |
| CN112530115B (en) | Electric power operation personnel protection against electric shock scene intelligence supervises integrated equipment | |
| KR101817350B1 (en) | Sensing Control Apparatus for Security Entrance of Walkway using Artificial Intelligence | |
| CN110082638A (en) | A kind of power matching network automatic inspection equipment and method | |
| CN108099957A (en) | A kind of locomotive shunting method and system based on detection of obstacles with identification | |
| CN111062373A (en) | Hoisting process danger identification method and system based on deep learning | |
| CN107230391A (en) | Actively anti-ship hits system and its application method to bridge | |
| CN111784954A (en) | Overhead transmission line external damage prevention alarm device and method | |
| CN206322303U (en) | The power construction safety early warning device of laser combination electric field | |
| CN114283544A (en) | Railway platform intrusion monitoring system and method based on artificial intelligence | |
| CN213424017U (en) | Intelligent construction site system | |
| CN207089322U (en) | A kind of Railway Site operation bidirectional security system based on monocular camera machine vision | |
| CN114120656B (en) | Comprehensive solution system for urban viaduct road safety protection | |
| CN115102292A (en) | Cable external-damage-prevention monitoring method and system | |
| CN115311560A (en) | Method and device for identifying hidden danger of external damage of power transmission channel | |
| CN115321401A (en) | Intelligent endless rope winch electric control system | |
| CN112070999B (en) | Underground pipe network protection warning board and vibration signal identification method thereof | |
| CN211509200U (en) | Transmission line prevents outer broken monitored control system based on artificial intelligence technique | |
| CN203326525U (en) | Novel high voltage line bumper | |
| CN219143006U (en) | Transmission line monitoring facilities |
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 |