CN110146789B - Intelligent operation and inspection reporting method and device - Google Patents
Intelligent operation and inspection reporting method and device Download PDFInfo
- Publication number
- CN110146789B CN110146789B CN201910566969.8A CN201910566969A CN110146789B CN 110146789 B CN110146789 B CN 110146789B CN 201910566969 A CN201910566969 A CN 201910566969A CN 110146789 B CN110146789 B CN 110146789B
- Authority
- CN
- China
- Prior art keywords
- fault
- tower
- reason
- neural network
- training
- 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.)
- Active
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000013528 artificial neural network Methods 0.000 claims abstract description 57
- 230000005540 biological transmission Effects 0.000 claims abstract description 36
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 13
- 238000012549 training Methods 0.000 claims description 54
- 230000001932 seasonal effect Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 15
- 208000025274 Lightning injury Diseases 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000004927 fusion Effects 0.000 description 5
- 238000012423 maintenance Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Navigation (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention provides an intelligent operation and inspection reporting method and device. The intelligent operation and inspection reporting method comprises the steps of judging the position of a power transmission line fault tower and the fault reason, and comprises the following steps: obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower; selecting at least two fault characteristics matched with the fault reason to train a neural network, and judging the fault reason; calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map; and filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display. The power failure time of the fault can be reduced, and the power supply reliability is improved.
Description
Technical Field
The present disclosure belongs to the field of operation and inspection reporting, and in particular, to an intelligent operation and inspection reporting method and apparatus.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of promoting the deep fusion of the internet plus and the traditional business, the intelligent operation and inspection technology is developed rapidly, the improvement of the operation and inspection efficiency of the power grid can help to diagnose the fault of the power system rapidly and accurately, recover the normal working state of the power grid in time, guarantee the quality of power supply service, and have important significance for constructing a strong, reliable and self-healing intelligent power grid.
Because the transmission line is long, the number of towers is large, the problems of personnel shortage and heavy workload exist in the operation and maintenance, and the quality of the operation and maintenance work has strong dependence on operation and maintenance personnel. The inventor finds that at present, the operation and inspection work mainly depends on manual work to search a fault point, relevant personnel are informed by a dispatching department in a telephone mode when the fault occurs, the operation and inspection personnel determine the fault position by converting the position of a tower after receiving the telephone, and then reach the fault position through mobile phone navigation, so that long time is often needed, and the fault repairing time is delayed.
Disclosure of Invention
In order to solve the above problem, a first aspect of the present disclosure provides an intelligent operation and inspection notifying method, which can reduce a fault and power failure time and improve power supply reliability.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
an intelligent operation and inspection notification method comprises the following steps:
the method comprises the following steps of judging the position of a tower with a fault of the power transmission line and the fault reason, wherein the process comprises the following steps:
obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower;
selecting at least two fault characteristics matched with the fault reason to train a neural network, and judging the fault reason;
calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map;
and filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display.
Further, the fault features include meteorological data features, seasonal features, image recognition features, waveform features, historical fault features, and power transmission channel conditions; the fault causes are divided into four categories including lightning faults, engineering vehicles, mountain fires and equipment bodies.
The technical scheme has the advantages that the fault reason is judged by combining multi-source information fusion of meteorological features, seasonal features, image features, waveform features, historical fault features and power transmission channel condition information, the problem of fault and reason judgment which is difficult to solve under the traditional algorithm is solved, and the accuracy of fault reason judgment is improved.
Further, the process of training the neural network is as follows:
forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason;
inputting training samples in a training sample set into an initialized neural network with a preset structure;
calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold; otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than the preset condition threshold or the preset training stopping condition is met, and stopping the training of the neural network.
The technical scheme has the advantages that the neural network is trained, and the trained neural network is used for judging the fault reason, so that the calculation time for judging the fault reason is shortened, the efficiency for judging the fault reason is improved, and the timeliness of the operation and inspection report is further improved.
Further, the fault notification is pushed to the client side through the instant messaging server and displayed in an instant mode.
The technical scheme has the advantages that the instant messaging server is used for pushing the fault report, so that on one hand, the cost and time of program development are saved, on the other hand, the efficiency of operation and inspection report is improved, faults can be timely repaired, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
A second aspect of the present disclosure provides an intelligent operation and inspection notifying device.
An intelligent operation and inspection notifying device, comprising:
the fault position and reason judging module is used for judging the position of the power transmission line fault tower and the fault reason; the fault position and reason judgment module comprises:
the fault position judgment submodule is used for obtaining the distance between the current measuring point and the fault point by utilizing a fault distance measuring algorithm, converting the distance into a fault tower number and judging the position of the fault tower;
the fault cause judgment submodule selects at least two fault features matched with the fault causes to train a neural network and judges the fault causes;
the navigation map forming module is used for calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower and marking the routing inspection path on a corresponding map to form a navigation map;
and the fault report display module is used for filling the position of the fault tower, the fault reason and the navigation map into a preset fault report template to form a fault report, and pushing the fault report to the client for display.
Further, in the fault cause judgment submodule, the fault features comprise meteorological data features, seasonal features, image identification features, waveform features, historical fault features and power transmission channel conditions; the fault causes are divided into four categories including lightning faults, engineering vehicles, mountain fires and equipment bodies.
The technical scheme has the advantages that the fault reason is judged by combining multi-source information fusion of meteorological features, seasonal features, image features, waveform features, historical fault features and power transmission channel condition information, the problem of fault and reason judgment which is difficult to solve under the traditional algorithm is solved, and the accuracy of fault reason judgment is improved.
Further, in the fault cause judgment submodule, the process of training the neural network is as follows:
forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason;
inputting training samples in a training sample set into an initialized neural network with a preset structure;
calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold; otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than the preset condition threshold or the preset training stopping condition is met, and stopping the training of the neural network.
The technical scheme has the advantages that the neural network is trained, and the trained neural network is used for judging the fault reason, so that the calculation time for judging the fault reason is shortened, the efficiency for judging the fault reason is improved, and the timeliness of the operation and inspection report is further improved.
Further, in the fault notification display module, the fault notification is pushed to the client through the instant messaging server and displayed in an instant mode.
The technical scheme has the advantages that the instant messaging server is used for pushing the fault report, so that on one hand, the cost and time of program development are saved, on the other hand, the efficiency of operation and inspection report is improved, faults can be timely repaired, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
A third aspect of the disclosure provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the intelligent operation notification method as described above.
A fourth aspect of the present disclosure provides a computer device.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the intelligent operation notification method as described above.
The beneficial effects of this disclosure are:
(1) the method comprises the steps of calling a map database of a current routing inspection area, positioning the judged position of a fault tower in the map database, inquiring a routing inspection path from a current measuring point to the fault tower, and marking on a corresponding map to form a navigation map; the judged position and fault reason of the fault tower and the navigation map are filled into a preset fault reporting template to form fault reporting, and the fault reporting template is pushed to the client side to be displayed in time, so that the fault can be repaired in time, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
(2) The method is suitable for all power supply companies, the power companies can timely push fault information and navigation information to relevant operation and maintenance personnel through technology and cooperation of all departments, the fault condition can be accounted at the first time, the site is reached, and the fault can be quickly recovered.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of an intelligent operation inspection notification method according to an embodiment of the present disclosure.
Fig. 2 is a schematic structural diagram of an intelligent operation and inspection notifying device according to an embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 is a flowchart of an intelligent operation inspection notification method according to an embodiment of the present disclosure.
As shown in fig. 1, the intelligent operation and inspection notifying method of the embodiment includes:
s101: the method comprises the following steps of judging the position of a tower with a fault of the power transmission line and the fault reason, wherein the process comprises the following steps:
obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower;
and selecting at least two fault characteristics matched with the fault reason to train the neural network, and judging the fault reason.
In the embodiment, the fault characteristics comprise meteorological data characteristics, seasonal characteristics, image identification characteristics, waveform characteristics, historical fault characteristics and power transmission channel conditions;
the fault causes are divided into four categories including lightning faults, engineering vehicles, mountain fires and equipment bodies.
Specifically, meteorological data characteristics: shade, season, time, temperature, humidity, wind power and terrain in a 10-kilometer grid area near the power transmission line.
Seasonal characteristics: spring (3-5 months), summer (6-8 months), autumn (9-11 months), winter (12-2 months)
Image recognition features: engineering truck, mountain fire, lightning stroke trace and equipment abnormity.
Waveform characteristics: metallic, current waveform, zero sequence current high frequency harmonic, zero sequence current direct current component, wavelet packet energy.
Historical failure characteristics: the lightning stroke fault frequency is high, the fault frequency caused by the engineering vehicle is high, and the fault caused by the equipment body is high.
The condition of a power transmission channel: a pattern within a 5 km radius along the line.
Fault signature table, as shown in table 1:
TABLE 1 Fault signature Table
It is understood that in other embodiments, the fault characteristics include, but are not limited to, meteorological data characteristics, seasonal characteristics, image recognition characteristics, waveform characteristics, historical fault characteristics, and power transmission channel conditions, and may be a combination of any at least two of the meteorological data characteristics, seasonal characteristics, image recognition characteristics, waveform characteristics, historical fault characteristics, and power transmission channel conditions, as may be selected by one of ordinary skill in the art depending on the particular situation.
The failure causes can be divided into other categories, and those skilled in the art can divide the failure causes according to specific situations.
The technical scheme has the advantages that the fault reason is judged by combining multi-source information fusion of meteorological features, seasonal features, image features, waveform features, historical fault features and power transmission channel condition information, the problem of fault and reason judgment which is difficult to solve under the traditional algorithm is solved, and the accuracy of fault reason judgment is improved.
In a specific implementation, the process of training the neural network is as follows:
1) forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason;
specifically, the fault characteristic value is determined as follows:
meteorological features: thunderstorm is represented by 1, overcast by 0.5, and rain by 0.
Seasonal characteristics: spring, summer, autumn and winter are respectively represented by 0, 0.25, 0.5 and 1.
Image characteristics: lightning stroke trace is 1, engineering truck is 2, mountain fire is 3, and equipment abnormity is 4.
Waveform characteristics: and calculating first and second order differential of the fault voltage and current sampling data. And (3) calculating a numerical differential by adopting Lagrangian function interpolation and Richardson extrapolation methods for the discrete points to serve as a metallic and high-resistance numerical value.
The zero sequence current is decomposed into a series of sine quantity sums with the frequency being positive integral multiple of the power frequency by a Fourier series expansion method. And converting the fault recording sampling time domain signal into a frequency domain signal, namely performing 3-time harmonic analysis on the harmonic and direct current content characteristics of the fault zero sequence current to obtain a corresponding numerical value.
And extracting effective fault meteorological features and numerical features from fault recording data, related meteorological information, power transmission channel information, image identification information and historical faults of a given sample.
2) And inputting the training samples in the training sample set into the initialized neural network with the preset structure.
Specifically, in the process of initializing the neural network, the input level nodes of the neural network read the input amount of the samples and set the random initial weight and the threshold.
3) Calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold; otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than the preset condition threshold or a preset training stopping condition is met (for example, the training times exceed n times, wherein n is a preset known number, such as 20 times), and stopping the training of the neural network.
Specifically, the signal is transmitted from the input layer to the hidden layer and the output layer in sequence, and the output value of the last layer is obtained through calculation; if the error precision meets the set condition threshold value and is less than 0.001, finishing training and outputting a result, otherwise, feeding errors back to the input layer from the output layer and the hidden layer in sequence, calculating the correction quantity of the weight value and the threshold value of each layer according to a gradient descent method, and continuing training according to the new weight value and the threshold value until the precision of the output result meets the requirement or meets a preset training stopping condition (for example, the training times exceed n times, wherein n is a preset known number, such as 20 times).
It should be noted that the neural network may be a BP neural network or a CNN neural network, and those skilled in the art may specifically select a structural form of the neural network according to actual situations.
The technical scheme has the advantages that the neural network is trained, and the trained neural network is used for judging the fault reason, so that the calculation time for judging the fault reason is shortened, the efficiency for judging the fault reason is improved, and the timeliness of the operation and inspection report is further improved.
S102: and calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map.
And inquiring the stored tower coordinates according to the position of the tower with the fault, determining the longitude and latitude of the fault, inquiring the routing inspection path, and displaying the routing inspection path on the map.
The form of tower information storage is shown in table 2.
TABLE 2 Tower information storage Format
| Serial number | Attribute name | Description of the invention | Type (B) |
| 1 | id | Record number | Long |
| 2 | faultTime | Recording time | Time |
| 3 | text | Text information (failure diagnosis result) | String |
| 4 | picFile | png picture file stream | byte[] |
| 5 | GPS | Position coordinates of tower | String |
| 6 | contacts | Receiver person | String |
Specifically, the current coordinates are accurately positioned by the GPS to serve as a starting place, the coordinates of a fault tower serve as a target, and an accurate path reaching the tower is formed. In the specific implementation, a map database of the current routing inspection area is obtained through an API (application program interface) communication protocol to form path navigation.
S103: and filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display.
It should be noted that the fault notification template is preset, and may be in the form of a text expression + a navigation map of the position and the cause of the fault tower, or in the form of a text expression + a fault waveform map + a navigation map of the position and the cause of the fault tower, and a person in the art may specifically set the fault notification template according to actual conditions.
For example: and the text expression of the position of the fault tower and the fault reason is shown in the table 3.
TABLE 3 text presentation of the location of the faulty tower and the cause of the fault
As an embodiment, the failure notification is pushed to the client via the instant messaging server and displayed instantly.
In this example, the instant messaging server is an enterprise WeChat Server.
It is understood that the instant messaging server may also be a WeChat server, an update server, etc., and those skilled in the art may specifically select an instant messaging server corresponding to a corresponding type of instant messaging method according to actual situations.
The technical scheme has the advantages that the instant messaging server is used for pushing the fault report, so that on one hand, the cost and time of program development are saved, on the other hand, the efficiency of operation and inspection report is improved, faults can be timely repaired, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
In the embodiment, a map database of a current routing inspection area is retrieved, the judged position of a fault tower is positioned in the map database, a routing inspection path from a current measuring point to the fault tower is inquired and marked on a corresponding map, and a navigation map is formed; the judged position and fault reason of the fault tower and the navigation map are filled into a preset fault reporting template to form fault reporting, and the fault reporting template is pushed to the client side to be displayed in time, so that the fault can be repaired in time, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
The embodiment is suitable for all power supply companies, and the power company can timely push fault information and navigation information to relevant operation and maintenance and repair personnel through the cooperation of the technology and all departments, so that the fault condition can be accounted and repaired on the spot at the first time, and the fault can be quickly recovered.
Example two
Fig. 2 is a schematic structural diagram of an intelligent operation and inspection notifying device according to an embodiment of the present disclosure.
As shown in fig. 2, the intelligent operation and inspection notifying device of the present embodiment includes:
(1) and the fault position and reason judgment module is used for judging the position of the power transmission line fault tower and the fault reason.
Specifically, the fault location and reason determining module includes:
(1.1) a fault position judgment submodule, which is used for obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number and judging the position of the fault tower;
(1.2) the fault reason judgment submodule selects at least two fault characteristics matched with the fault reason to train a neural network and judges the fault reason;
as a specific implementation manner, in the fault cause judgment sub-module, the fault features include meteorological data features, seasonal features, image identification features, waveform features, historical fault features and power transmission channel conditions; the fault causes are divided into four categories including lightning faults, engineering vehicles, mountain fires and equipment bodies.
It is understood that in other embodiments, the fault characteristics include, but are not limited to, meteorological data characteristics, seasonal characteristics, image recognition characteristics, waveform characteristics, historical fault characteristics, and power transmission channel conditions, and may be a combination of any at least two of the meteorological data characteristics, seasonal characteristics, image recognition characteristics, waveform characteristics, historical fault characteristics, and power transmission channel conditions, as may be selected by one of ordinary skill in the art depending on the particular situation.
The failure causes can be divided into other categories, and those skilled in the art can divide the failure causes according to specific situations.
The technical scheme has the advantages that the fault reason is judged by combining multi-source information fusion of meteorological features, seasonal features, image features, waveform features, historical fault features and power transmission channel condition information, the problem of fault and reason judgment which is difficult to solve under the traditional algorithm is solved, and the accuracy of fault reason judgment is improved.
As a specific implementation manner, in the fault cause judgment sub-module, the process of training the neural network is as follows:
forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason;
inputting training samples in a training sample set into an initialized neural network with a preset structure;
calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold (for example, the error precision meets the requirement that the set condition threshold is smaller than 0.001); otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than the preset condition threshold or a preset training stopping condition is met (for example, the training times exceed n times, wherein n is a preset known number, such as 20 times), and stopping training of the neural network.
It should be noted that the neural network may be a BP neural network or a CNN neural network, and those skilled in the art may specifically select a structural form of the neural network according to actual situations.
The technical scheme has the advantages that the neural network is trained, and the trained neural network is used for judging the fault reason, so that the calculation time for judging the fault reason is shortened, the efficiency for judging the fault reason is improved, and the timeliness of the operation and inspection report is further improved.
(2) And the navigation map forming module is used for calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking the routing inspection path on a corresponding map to form a navigation map.
(3) And the fault report display module is used for filling the position of the fault tower, the fault reason and the navigation map into a preset fault report template to form a fault report, and pushing the fault report to the client for display.
It should be noted that the fault notification template is preset, and may be in the form of a text expression + a navigation map of the position and the cause of the fault tower, or in the form of a text expression + a fault waveform map + a navigation map of the position and the cause of the fault tower, and a person in the art may specifically set the fault notification template according to actual conditions.
As an embodiment, in the failure notification presentation module, the failure notification is pushed to the client via the instant messaging server and displayed in real time.
In this example, the instant messaging server is an enterprise WeChat Server.
It is understood that the instant messaging server may also be a WeChat server, an update server, etc., and those skilled in the art may specifically select an instant messaging server corresponding to a corresponding type of instant messaging method according to actual situations.
The technical scheme has the advantages that the instant messaging server is used for pushing the fault report, so that on one hand, the cost and time of program development are saved, on the other hand, the efficiency of operation and inspection report is improved, faults can be timely repaired, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
In the embodiment, a map database of a current routing inspection area is retrieved, the judged position of a fault tower is positioned in the map database, a routing inspection path from a current measuring point to the fault tower is inquired and marked on a corresponding map, and a navigation map is formed; the judged position and fault reason of the fault tower and the navigation map are filled into a preset fault reporting template to form fault reporting, and the fault reporting template is pushed to the client side to be displayed in time, so that the fault can be repaired in time, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
EXAMPLE III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
the method comprises the following steps of judging the position of a tower with a fault of the power transmission line and the fault reason, wherein the process comprises the following steps:
obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower;
selecting at least two fault characteristics matched with the fault reason to train a neural network, and judging the fault reason;
calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map;
and filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display.
In the embodiment, a map database of a current routing inspection area is retrieved, the judged position of a fault tower is positioned in the map database, a routing inspection path from a current measuring point to the fault tower is inquired and marked on a corresponding map, and a navigation map is formed; the judged position and fault reason of the fault tower and the navigation map are filled into a preset fault reporting template to form fault reporting, and the fault reporting template is pushed to the client side to be displayed in time, so that the fault can be repaired in time, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
Example four
The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the following steps:
the method comprises the following steps of judging the position of a tower with a fault of the power transmission line and the fault reason, wherein the process comprises the following steps:
obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower;
selecting at least two fault characteristics matched with the fault reason to train a neural network, and judging the fault reason;
calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map;
and filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display.
In the embodiment, a map database of a current routing inspection area is retrieved, the judged position of a fault tower is positioned in the map database, a routing inspection path from a current measuring point to the fault tower is inquired and marked on a corresponding map, and a navigation map is formed; the judged position and fault reason of the fault tower and the navigation map are filled into a preset fault reporting template to form fault reporting, and the fault reporting template is pushed to the client side to be displayed in time, so that the fault can be repaired in time, the fault power failure time is reduced, the power supply reliability is improved, and the stable operation of the whole power transmission line is ensured.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (6)
1. An intelligent operation and inspection reporting method is characterized by comprising the following steps:
the method comprises the following steps of judging the position of a tower with a fault of the power transmission line and the fault reason, wherein the process comprises the following steps:
obtaining the distance between the current measuring point and the fault point by using a fault distance measuring algorithm, converting the distance into a fault tower number, and judging the position of the fault tower;
selecting at least two fault characteristics matched with the fault reason to train a neural network, and judging the fault reason; the fault characteristics comprise meteorological data characteristics, seasonal characteristics, image identification characteristics, waveform characteristics, historical fault characteristics and power transmission channel conditions; the failure reasons are divided into four categories, including lightning stroke failure, engineering vehicles, mountain fire and equipment bodies;
the process of training the neural network comprises the following steps: forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason; inputting training samples in a training sample set into an initialized neural network with a preset structure; calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold; otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than a preset condition threshold value or a preset training stopping condition is met, and stopping the training of the neural network;
calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower, and marking on a corresponding map to form a navigation map;
filling the position of the fault tower, the fault reason and the navigation map into a preset fault notification template to form a fault notification, and pushing the fault notification to a client for display;
the fault notification template is preset, can be in the form of character expression of the position and the reason of the fault tower and a navigation map, can also be in the form of character expression of the position and the reason of the fault tower, a fault oscillogram and a navigation map, and can be specifically set according to actual conditions.
2. The intelligent inspection notification method of claim 1, wherein the failure notification is pushed to the client via an instant messaging server and displayed instantly.
3. An intelligent operation and inspection reporting device, comprising:
the fault position and reason judging module is used for judging the position of the power transmission line fault tower and the fault reason; the fault position and reason judgment module comprises:
the fault position judgment submodule is used for obtaining the distance between the current measuring point and the fault point by utilizing a fault distance measuring algorithm, converting the distance into a fault tower number and judging the position of the fault tower;
the fault cause judgment submodule selects at least two fault features matched with the fault causes to train a neural network and judges the fault causes; the fault characteristics comprise meteorological data characteristics, seasonal characteristics, image identification characteristics, waveform characteristics, historical fault characteristics and power transmission channel conditions; the failure reasons are divided into four categories, including lightning stroke failure, engineering vehicles, mountain fire and equipment bodies;
the process of training the neural network in the fault reason judgment submodule is as follows: forming a training sample set according to a known fault reason and at least two fault characteristics matched with the known fault reason; inputting training samples in a training sample set into an initialized neural network with a preset structure; calculating the error output by the neural network, and finishing training if the error range is smaller than a preset condition threshold; otherwise, adjusting parameters in the neural network to continue training until the error range is smaller than a preset condition threshold value or a preset training stopping condition is met, and stopping the training of the neural network;
the navigation map forming module is used for calling a map database of the current routing inspection area, positioning the position of the fault tower in the map database, inquiring a routing inspection path from the current measuring point to the fault tower and marking the routing inspection path on a corresponding map to form a navigation map;
and the fault report display module is used for filling the position of the fault tower, the fault reason and the navigation map into a preset fault report template to form a fault report, and pushing the fault report to the client for display.
4. The intelligent operation and inspection notification device as claimed in claim 3, wherein in the failure notification presentation module, the failure notification is pushed to the client via the instant communication server and displayed in real time.
5. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps in the intelligent traffic notification method according to any one of claims 1-2.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps in the intelligent traffic notification method according to any of claims 1-2 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910566969.8A CN110146789B (en) | 2019-06-27 | 2019-06-27 | Intelligent operation and inspection reporting method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910566969.8A CN110146789B (en) | 2019-06-27 | 2019-06-27 | Intelligent operation and inspection reporting method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110146789A CN110146789A (en) | 2019-08-20 |
| CN110146789B true CN110146789B (en) | 2021-11-16 |
Family
ID=67596564
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910566969.8A Active CN110146789B (en) | 2019-06-27 | 2019-06-27 | Intelligent operation and inspection reporting method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110146789B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111123087A (en) * | 2019-12-24 | 2020-05-08 | 深圳供电局有限公司 | Power distribution network switching equipment fault detection method and device and computer equipment |
| CN111275215A (en) * | 2020-02-29 | 2020-06-12 | 云南电网有限责任公司西双版纳供电局 | Voltage monitor fault self-diagnosis method and system based on Internet of things |
| CN112910691B (en) * | 2021-01-19 | 2023-06-27 | 中国工商银行股份有限公司 | Machine room fault detection method and device |
| CN113379193B (en) * | 2021-05-11 | 2023-04-18 | 国网河北省电力有限公司邢台供电分公司 | Power grid operation inspection control method and device and terminal equipment |
| CN113705836A (en) * | 2021-08-31 | 2021-11-26 | 国网山东省电力公司微山县供电公司 | System and device for power grid field operation guidance |
| CN116991940B (en) * | 2023-07-25 | 2024-02-27 | 国网安徽省电力有限公司淮北供电公司 | Distributed multi-layer data processing method and system based on power transmission and transformation big data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103606205A (en) * | 2013-11-19 | 2014-02-26 | 云南电网公司昭通供电局 | Tour-inspection device and inspection system for power transmission line |
| CN105761320A (en) * | 2016-01-27 | 2016-07-13 | 国家电网公司 | Power line inspection system |
| CN106199657A (en) * | 2016-09-07 | 2016-12-07 | 国网辽宁省电力有限公司辽阳供电公司 | A kind of electric power line pole tower Big Dipper GPS dual-mode Position Fixing Navigation System |
| CN106326402A (en) * | 2016-08-22 | 2017-01-11 | 国网山东省电力公司青岛供电公司 | Power transmission line information inquiry method and device and mobile terminal |
| CN107271843A (en) * | 2017-06-16 | 2017-10-20 | 广东电网有限责任公司佛山供电局 | Transmission open acess, facility information inquiry and navigation system and method |
| CN108981697A (en) * | 2018-05-25 | 2018-12-11 | 国网山西省电力公司晋城供电公司 | Electric power line pole tower refines polling path and positions application apparatus |
| CN109270407A (en) * | 2018-11-16 | 2019-01-25 | 国网山东省电力公司电力科学研究院 | Extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion |
-
2019
- 2019-06-27 CN CN201910566969.8A patent/CN110146789B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103606205A (en) * | 2013-11-19 | 2014-02-26 | 云南电网公司昭通供电局 | Tour-inspection device and inspection system for power transmission line |
| CN105761320A (en) * | 2016-01-27 | 2016-07-13 | 国家电网公司 | Power line inspection system |
| CN106326402A (en) * | 2016-08-22 | 2017-01-11 | 国网山东省电力公司青岛供电公司 | Power transmission line information inquiry method and device and mobile terminal |
| CN106199657A (en) * | 2016-09-07 | 2016-12-07 | 国网辽宁省电力有限公司辽阳供电公司 | A kind of electric power line pole tower Big Dipper GPS dual-mode Position Fixing Navigation System |
| CN107271843A (en) * | 2017-06-16 | 2017-10-20 | 广东电网有限责任公司佛山供电局 | Transmission open acess, facility information inquiry and navigation system and method |
| CN108981697A (en) * | 2018-05-25 | 2018-12-11 | 国网山西省电力公司晋城供电公司 | Electric power line pole tower refines polling path and positions application apparatus |
| CN109270407A (en) * | 2018-11-16 | 2019-01-25 | 国网山东省电力公司电力科学研究院 | Extra high voltage direct current transmission line failure cause discrimination method based on Multi-source Information Fusion |
Non-Patent Citations (1)
| Title |
|---|
| 输电线路手机卫星地图导航系统的定制与应用探究;王冠宇;《机电信息》;20151231(第33期);第41、43页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110146789A (en) | 2019-08-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110146789B (en) | Intelligent operation and inspection reporting method and device | |
| US20190301963A1 (en) | Processing data for predicting pipe failure | |
| CN102116639B (en) | Method and device for automatically checking traffic limit information of electronic map | |
| CN104992254B (en) | Mountain fire based on power grid GIS and the meteorological trend pre-judging method combined | |
| CN108320515B (en) | Automatic matching and checking method and system for road network of traffic accident site | |
| CN104182594A (en) | Method for drawing power system wind area graph | |
| CN104376383A (en) | Grid voltage monitoring and prediction system and method based on geographic information system | |
| CN108204819B (en) | Map data automatic detection method and device and hybrid navigation system | |
| CN113298295B (en) | Meteorological forecast system for power production | |
| CN104504616A (en) | Positioning method for electric network equipment with operating risk based on GIS (geographic information system) and weather information | |
| CN113409482A (en) | Optical cable line inspection monitoring and work quantification assessment method | |
| CN110910440B (en) | Power transmission line length determination method and system based on power image data | |
| CN111879338B (en) | Electronic map-based driving track planning compensation method and device | |
| CN108090722A (en) | A kind of logistics network station recognition equipment and recognition methods based on GPS | |
| CN115170980B (en) | Gas pipeline high consequence area intelligent identification method based on satellite image identification | |
| CN105069100A (en) | Power network geographic information system (GIS) based mobile terminal collaborative plotting method | |
| CN104965854A (en) | Automatic plotting method and system for material allocation based on electrical network GIS | |
| CN112766689B (en) | Method and device for dividing patrol route of power transmission line | |
| CN108513306B (en) | Network coverage prediction method based on test big data | |
| CN110070242B (en) | Gas pipeline high consequence area identification and evaluation system and method | |
| CN104268794A (en) | Drawing method of power transmission line bird damage distribution diagram | |
| CN113537846A (en) | Meteorological disaster-based risk analysis method and system for power transmission and distribution line tower | |
| CN109446286B (en) | Visualization method and device for highway road network map | |
| CN117610929A (en) | Method and system for identifying and evaluating risks in regions with severe wind disaster along railway | |
| CN105203926A (en) | Method for improving fault location accuracy of extra-high-voltage DC transmission line |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |