CN113052474B - Cross-crossing key part early warning method and system based on power grid - Google Patents
Cross-crossing key part early warning method and system based on power grid Download PDFInfo
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Abstract
The application discloses a crossing key part early warning method and system based on a power grid, and relates to the field of power grid line crossing. The power grid-based crossing key part early warning method comprises the following steps: acquiring first initial data of a power grid line to be early-warned, wherein the first initial data comprises a three-dimensional image of the power grid line; determining a crossing position of the power grid line based on the three-dimensional image, and then collecting second initial data of the crossing position; inputting the second initial data into a preset cross defect diagnosis model, and then screening the second initial data to obtain target early warning data; and generating early warning prompt information according to the target early warning data, and carrying out early warning prompt on the power grid line according to the early warning prompt information. The grid-based cross key part early warning method can automatically diagnose and early warn and analyze the cross point line, improves early warn precision, and can better manage hidden danger risk points in the line.
Description
Technical Field
The application relates to the field of power grid line crossing, in particular to a power grid-based crossing key part early warning method and system.
Background
At present, the crossing point lines in the power grid are denser, so that the risk points in the crossing point lines are more, specifically: the distance change of the wires to the wires and the wires to the ground is easy to cause the tripping condition of the transmission line, but the traditional cross crossing point operation and maintenance measures have strong dependence on operation and maintenance personnel, and the precision is not high, so that hidden danger risk points in the comprehensive and accurate management and control line are difficult to achieve.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the method and the system for early warning of the crossing key parts based on the power grid can automatically diagnose and early warn and analyze the crossing point line, improve early warn precision and better manage hidden danger risk points in the line.
According to an embodiment of the first aspect of the application, a power grid-based cross-over critical part early warning method comprises the following steps:
acquiring first initial data of a power grid line to be pre-warned through a laser detector on an unmanned aerial vehicle, wherein the first initial data are circuit attribute information of the power grid line, and the circuit attribute information comprises a three-dimensional image of the power grid line;
Determining a crossing of the grid line based on the three-dimensional image;
collecting second initial data of the crossing position, wherein the second initial data is circuit attribute information of the crossing position;
inputting the second initial data into a preset cross defect diagnosis model, wherein the cross defect diagnosis model comprises a data processing module;
screening the second initial data according to the data processing module to obtain target early warning data;
generating early warning prompt information according to the target early warning data;
and carrying out early warning prompt on the power grid line according to the early warning prompt information.
The grid-based cross-over key part early warning method has at least the following beneficial effects:
acquiring first initial data of a power grid line to be pre-warned by a laser detector on the unmanned aerial vehicle, wherein the first initial data comprises a three-dimensional image of the power grid line; determining a crossing position of the power grid line based on the three-dimensional image, and then collecting second initial data of the crossing position; inputting the second initial data into a preset cross defect diagnosis model, and then screening the second initial data to obtain target early warning data; and generating early warning prompt information according to the target early warning data, and carrying out early warning prompt on the power grid line according to the early warning prompt information. The grid-based cross key part early warning method can automatically diagnose and early warn and analyze the cross point line, improves early warn precision, and can better manage hidden danger risk points in the line.
According to some embodiments of the application, the circuit attribute information further includes three-dimensional coordinates of the grid line;
the obtaining of the first initial data of the power grid line to be pre-warned through the laser detector on the unmanned aerial vehicle comprises the following steps:
determining the position range of a power grid line to be pre-warned;
determining a flight path of the unmanned aerial vehicle according to the position range;
acquiring a three-dimensional image and position information acquired by a laser detector on the unmanned aerial vehicle based on the flight route;
and obtaining the three-dimensional coordinates of the power grid line according to the three-dimensional image and the position information.
According to some embodiments of the application, the determining the crossing of the grid line based on the three-dimensional image includes:
extracting line attribute information of the power grid line from the three-dimensional image;
and acquiring the crossing position of the power grid line according to the line attribute information to obtain the crossing position.
According to some embodiments of the present application, the screening the second initial data according to the data processing module to obtain target early warning data includes:
determining, by the data processing module, a first fault region at the crossover based on the second initial data, the first fault region including third initial data, the third initial data being circuit attribute information of the first fault region;
Constructing an initial fault set according to the third initial data and preset key line information;
and screening the initial fault set to obtain target early warning data.
According to some embodiments of the present application, the screening the initial fault set to obtain target early warning data includes:
acquiring a plurality of initial weights corresponding to a plurality of preset attribute information;
determining a plurality of target weights corresponding to a plurality of circuit attribute information in the crossing position according to a plurality of initial weights corresponding to a plurality of preset attribute information;
and screening out target early warning data from the initial fault set according to the target weight corresponding to each attribute information.
According to some embodiments of the present application, the screening the target early warning data from the initial fault set according to the target weight corresponding to each attribute information includes:
sequencing the target weights to obtain a sequencing result;
and screening out target early warning data from the initial fault set based on the sorting result, wherein each target early warning data comprises the target weight.
According to some embodiments of the present application, the early warning prompt information includes early warning time and early warning frequency;
Correspondingly, the generating the early warning prompt information according to the target early warning data includes:
and determining early warning time and early warning frequency according to the size of the target weight.
A grid-based cross-over critical section early warning system according to an embodiment of a second aspect of the present application, comprising:
the acquisition module is used for: the acquisition module is used for acquiring first initial data of a power grid line to be pre-warned through a laser detector on the unmanned aerial vehicle, wherein the first initial data is circuit attribute information of the power grid line, and the circuit attribute information comprises a three-dimensional image of the power grid line;
and a crossing module: the crossing module determines a crossing of the grid line based on the three-dimensional image;
and the acquisition module is used for: the acquisition module is used for acquiring second initial data of the crossing part, wherein the second initial data is circuit attribute information of the crossing part;
an input module: the input module is used for inputting the second initial data into a preset cross defect diagnosis model, and the cross defect diagnosis model comprises a data processing module;
and a screening module: the screening module is used for screening the second initial data according to the data processing module to obtain target early warning data;
And the early warning module is used for: the early warning module is used for generating early warning prompt information according to the target early warning data;
the grid-based cross-over key part early warning system has at least the following beneficial effects:
the power grid-based cross-over key part early warning system comprises an acquisition module, a cross module, an acquisition module, an input module, a screening module and an early warning module, wherein the acquisition module acquires first initial data of a power grid line to be early warned through a laser detector on an unmanned aerial vehicle, and the first initial data comprises a three-dimensional image of the power grid line; the crossing module determines a crossing position of the power grid line based on the three-dimensional image, and the acquisition module acquires second initial data of the crossing position; the input module inputs the second initial data to a preset cross defect diagnosis model, and the screening module screens the second initial data to obtain target early warning data; the early warning module generates early warning prompt information according to the target early warning data, and carries out early warning prompt on the power grid line according to the early warning prompt information. The grid-based crossing key part early warning system can automatically diagnose and early warn and analyze crossing point lines, improves early warn precision, and can better manage hidden danger risk points in the lines.
A grid-based cross-over critical section early warning system according to an embodiment of a third aspect of the present application, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions that are executed by the at least one processor to cause the at least one processor to implement a grid-based cross-critical-site warning method according to an embodiment of the first aspect of the present invention when the instructions are executed.
A grid-based cross-over critical section early warning system according to an embodiment of a fourth aspect of the present application, comprising:
the grid-based cross-over critical section warning system stores computer executable instructions for performing the grid-based cross-over critical section warning method according to the embodiment of the first aspect of the present invention.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The application is further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a grid-based cross-over critical-part early warning method provided in some embodiments of the present application;
FIG. 2 is a block diagram of a modular architecture of a grid-based cross-over critical-site early warning system provided in some embodiments of the present application;
fig. 3 is a schematic hardware structure diagram of a grid-based cross-over critical part early warning system according to some embodiments of the present application.
Reference numerals: acquisition module 100, crossover module 200, acquisition module 300, input module 400, screening module 500, pre-warning module 600, processor 700, memory 800, input/output interface 900, communication interface 1000, bus 1100.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present disclosure.
In the description of the present application, a description with reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
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. The terminology used herein is for the purpose of describing embodiments of the present disclosure only and is not intended to be limiting of the present disclosure.
At present, the crossing point lines in the power grid are denser, so that the risk points in the crossing point lines are more, specifically: the distance change of the wires to the wires and the wires to the ground is easy to cause the tripping condition of the transmission line, but the traditional cross crossing point operation and maintenance measures have strong dependence on operation and maintenance personnel, and the precision is not high, so that hidden danger risk points in the comprehensive and accurate management and control line are difficult to achieve.
Based on the method and the system, the first initial data of the power grid line to be pre-warned can be obtained through a laser detector on the unmanned aerial vehicle, wherein the first initial data comprises a three-dimensional image of the power grid line; determining a crossing position of the power grid line based on the three-dimensional image, and then collecting second initial data of the crossing position; inputting the second initial data into a preset cross defect diagnosis model, and then screening the second initial data to obtain target early warning data; and generating early warning prompt information according to the target early warning data, and carrying out early warning prompt on the power grid line according to the early warning prompt information. The grid-based cross key part early warning method can automatically diagnose and early warn and analyze the cross point line, improves early warn precision, and can better manage hidden danger risk points in the line.
In a first aspect, an embodiment of the present application provides a grid-based cross-over critical part early warning method.
Referring to fig. 1, fig. 1 is a flowchart of a grid-based cross-key part early warning method according to some embodiments of the present application, and specifically includes the steps of:
S100, acquiring first initial data of a power grid line to be pre-warned by a laser detector on the unmanned aerial vehicle, wherein the first initial data is circuit attribute information of the power grid line, and the circuit attribute information comprises a three-dimensional image of the power grid line;
s200, determining a crossing position of a power grid line based on the three-dimensional image;
s300, collecting second initial data at the crossing position, wherein the second initial data is circuit attribute information at the crossing position;
s400, inputting second initial data into a preset cross defect diagnosis model, wherein the cross defect diagnosis model comprises a data processing module;
s500, screening the second initial data according to the data processing module to obtain target early warning data;
s600, generating early warning prompt information according to the target early warning data;
and S700, carrying out early warning prompt on the power grid line according to the early warning prompt information.
In step S100, first initial data of a power grid line to be pre-warned is obtained through a laser detector on an unmanned aerial vehicle (Unmanned Aerial Vehicle/Drones, UAV), the embodiment of the application may detect basic conditions of the power grid line through the unmanned aerial vehicle, where the unmanned aerial vehicle is an unmanned aerial vehicle operated by using a radio remote control device and a self-provided program control device, or is operated completely or intermittently and autonomously by a vehicle-mounted computer, so that the power grid line data can be automatically collected, a problem of inefficiency caused by manual collection is avoided, the first initial data obtained through the aurora detector includes circuit attribute information of the power grid line, where the circuit attribute information may be names, voltage values, circuit numbers, circuit splitting numbers, spatial configuration structures, associated position areas and the like of a plurality of lines in the power grid, the spatial configuration results may be a circuit space form obtained by arranging triangle, level, vertical, horizontal and the like of the power grid line, the three-dimensional image of the power grid line, and the three-dimensional image of the power grid may be the cloud data of the power grid, the digital elevation model (Digital Elevation Model, DEM), the digital surface model (Digital Surface Model), and the digital image model (Digital Orthophoto Map). The digital elevation model realizes the digital simulation of the ground terrain through the limited terrain elevation data, namely the digital expression of the terrain surface morphology, is a physical ground model which represents the ground elevation in the form of a group of ordered value arrays, is a branch of the digital terrain model, and can be derived from various other terrain characteristic values. Digital surface models are the generic term of surveying and mapping, referring to a collection of numerical representations of the surface morphology of an object. The digital orthophoto map is based on aerial photograph or remote sensing image, and is scanned, radiation correction, differential correction and mosaic are carried out pixel by pixel, the image data is cut according to the range of the topographic map, and the information of the topographic elements is added to the image plane in the forms of symbol, line drawing, annotation, kilometer grid, figure profile finishing and the like to form an image database stored in the form of grid data, and the image database has the geometric precision and image characteristics of the topographic map. Accurate attribute information of a power grid line can be obtained more comprehensively and accurately by acquiring the light point cloud data, the digital elevation model, the digital surface model and the digital orthophoto map of the power grid, and a reliable data base is provided for early warning of crossing key parts later.
In some embodiments, the circuit attribute information further includes three-dimensional coordinates of the grid line, and the step S100 specifically includes the steps of:
s110, determining a position range of a power grid line to be early-warned;
s120, determining a flight path of the unmanned aerial vehicle according to the position range;
s130, acquiring three-dimensional images and position information acquired by a laser detector on the unmanned aerial vehicle based on the flight route;
and S140, obtaining the three-dimensional coordinates of the power grid line according to the three-dimensional image and the position information.
In step S110, a location range of the power grid line to be pre-warned is determined, where the location range refers to a range where all the power grid lines to be pre-warned are located, and may be represented by two-dimensional coordinates.
In step S120, the position range of the power grid line is confirmed, the system automatically combines the current weather condition, visibility, wind speed of the unmanned aerial vehicle, and the like according to the shape and distance of the position range, and a suitable flight path is made, for example, a line with the shortest flight distance or a line with the widest coverage line range is designed, and a person skilled in the art can select according to the actual requirement, so that different requirements of users can be met, and in practical application, the line calibration can be realized by using the GPS (global positioning system ) positioning technology on the unmanned aerial vehicle.
In step S130, after the flight path is selected, the flight altitude may be further designed, and the system parameters of the unmanned aerial vehicle, such as the flight altitude, the flight speed, etc., are collected, for example, a three-dimensional image and position information may be collected, where the three-dimensional image refers to a three-dimensional image of the area to be pre-warned, and the position information refers to the position coordinates of the unmanned aerial vehicle during the flight, and generally, the three-dimensional coordinates of the unmanned aerial vehicle may be adopted.
In practical application, the data collected by the unmanned aerial vehicle can be processed through the laser radar to obtain a laser radar data graph, and software such as terra solid (commercial LiDAR data processing software) can be used for processing the data. The terra solid series software is a first set of commercial LiDAR data processing software, is developed based on a micro and runs on a Micorstation system, and comprises the following components: terraMatch, terraScan, terraModeler, terraPhoto, terraSurvey, terraPhoto Viewer, terra scan Viewer, terraPipe, terraSlave, terraPipeNet, etc., terra solid series software can rapidly load lidar (laser radar) point cloud data, under enough memory support (2G), loading 39000000 points only needs more than 40 seconds, and the terra solid software is utilized to process data acquired by the unmanned aerial vehicle, so that processing efficiency can be improved.
In step S140, the three-dimensional coordinates of the power grid line are obtained according to the three-dimensional image and the position information, the three-dimensional coordinates of the power grid line can be marked according to the three-dimensional image, and the three-dimensional coordinates can be further optimized by combining the position coordinates of the unmanned aerial vehicle in the flight process, so that the accuracy is improved.
In step S200, the crossing position of the grid line is determined based on the three-dimensional image, and the crossing position of the grid line, that is, the area where the early warning is required in the present application, may be determined from the three-dimensional image obtained in the above step.
In some embodiments, step S200 specifically includes the steps of:
s210, extracting line attribute information of a power grid line from a three-dimensional image;
s220, acquiring the crossing position of the power grid line according to the line attribute information, and obtaining the crossing position.
In step S210 and step S220, line attribute information of the power grid line is extracted from the three-dimensional image, and a crossing point of the power grid line is obtained according to the line attribute information. The line attribute information can be names, voltage values, associated positions, three-dimensional images and the like of a plurality of lines in the network, the crossing position indicates that a plurality of lines cross at a certain position, in this step, the line crossing position can be the attribute information of a certain line, such as the number of crossing lines A, or names or voltage values crossed with the lines A, stations at two ends, the lines A and the like, a plurality of crossing positions can be obtained according to the attribute information, such as the number of lines crossing the lines A is detected, if the number of lines crossing the lines A is greater than 1, the point is the crossing position, and the like, so that the actual situation of the crossing positions can be more comprehensively known, and a comprehensive early warning scheme can be formulated.
In practical application, according to the crossing position obtained in step S220, that is, the key position of the power grid line, the number of wires, the number of split wires and the space configuration structure of the power grid line are kept unchanged, and a catenary equation of the wires is calculated to obtain a vector expression of the wires:
y=ncos # ((x+c1)/n) +c2, where n is the tension of the lowest point of the wire of the power grid line, C1 and C2 are integral constants, the value of y is determined according to the position of the origin of coordinates and initial conditions, then the body line of the power grid line is obtained according to y, which crossing points are dangerous can be judged according to the obtained body line, early warning information is generated according to the danger index of the crossing points, and the judging method of the danger is as follows: for the geographical position information of the vicinity of the crossing, such as buildings, plants, roads, etc., if the geographical position information around the crossing is complex, it is indicated that the higher the risk index is, and then the early warning is given.
In practical application, important power grid lines can be marked, important power transmission channels with x important power grid lines are arranged and combined to form multiple combined faults, then multiple combined faults in the power transmission channels are constructed, and rapid power flow calculation is performed by combining the number of lines at the crossing position, the number of crossing roads and the number of crossing roads, or names or voltage values crossing with an A line, the attribute information of stations, the A line and the like at two ends, wherein the rapid power flow calculation method used in the embodiment of the application is as follows: gaussian Seidel (GS) and Newton-Raphson (NR), wherein Gaussian Seidel is an iterative method in numerical linear algebra and can be used for solving an approximate value solution linear equation set of a linear equation set solution, in many cases, the Gaussian Seidel is faster than a simple iterative method in convergence, the Newton-Raphson algorithm calculates a second derivative, namely a gradient direction on the premise of second-order minimality of an objective function, and the Newton-Rahson algorithm is a very effective optimizing method when the first derivative of an objective function of optimizing the second derivative and an inverse array of a sea-state array thereof are convenient to calculate according to the gradient direction.
In step S300, second initial data at the crossing position is collected, the second initial data is circuit attribute information at the crossing position, and since the first initial data of all lines of the power grid are collected in step S100, and the application focuses on the information at the crossing position, only the attribute information at the crossing position focused on, namely the second initial data, needs to be extracted from the first initial data, so that input initial data is reduced, and the system operation efficiency is improved.
In step S400, the second initial data is input to a preset cross defect diagnosis model, where the cross defect diagnosis model includes a data processing module, and the cross defect diagnosis model is used for detecting defects at a cross position, and making early warning and diagnosis schemes for the cross position according to detection results.
In step S500, the second initial data is filtered according to the data processing module to obtain target early warning data, where the target early warning data includes crossing points where early warning needs to be performed and corresponding early warning information.
In some embodiments, step S500 specifically includes the steps of:
s510, determining a first fault area at the crossing position through a data processing module based on second initial data, wherein the first fault area comprises third initial data, and the third initial data is circuit attribute information of the first fault area;
S520, constructing an initial fault set according to the third initial data and preset key line information;
and S530, screening the initial fault set to obtain target early warning data.
In step S510, a first fault area at the crossover is determined by the data processing module based on the second initial data, the first fault area including third initial data, the third initial data being circuit attribute information of the first fault area, that is, a place where a fault may exist at the crossover, that is, the first fault area, can be determined by the data processing module.
In step S520, an initial fault set is constructed according to the third initial data and the preset critical line information, and the area where the fault may exist and the preset critical line information are determined, so that the initial fault set can be constructed, where the critical line information refers to a marked critical line in the system or a line focused by the user, and the initial fault set includes crossing points where the fault may exist, and reasons, information, etc. of the fault may occur at the crossing points.
In step S530, the initial fault set is screened to obtain target early warning data, where the initial fault set is not the combination of the fault information at the crossing and the crossing, and the initial fault set needs to be screened according to the important, critical or possible fault degree to obtain target early warning data, and the target early warning data is used on the final early warning prompt.
In some embodiments, step S530 specifically includes the steps of:
s531, obtaining a plurality of initial weights corresponding to a plurality of preset attribute information;
s532, determining a plurality of target weights corresponding to a plurality of circuit attribute information in the crossing position according to a plurality of initial weights corresponding to a plurality of preset attribute information;
and S533, screening out target early warning data from the initial fault set according to the target weight corresponding to each attribute information.
In step S531, a plurality of initial weights corresponding to the plurality of preset attribute information are obtained, for example, the number of lines at the crossing point, the number of crossing roads, or the name or voltage value of crossing with the line a, the costs of the plant stations and the equipment at the two ends, etc. are mentioned above, and each preset attribute has a plurality of initial weights.
In step S532, a plurality of target weights corresponding to a plurality of circuit attribute information at the crossing point are determined according to a plurality of initial weights corresponding to a plurality of preset attribute information, where the target weights are weights corresponding to each attribute information at the crossing point, for example, if the number of lines at the crossing point is 1, the preset weight is 0.1, if the number of lines is 10, the preset weight is 0.5, and if the number of lines at the crossing point is 10, the target weight is 0.5.
In step S533, the target early-warning data is screened from the initial failure set according to the target weight corresponding to each attribute information, specifically, the target weights corresponding to each attribute information are directly added to obtain an early-warning score, or the target weights corresponding to each attribute information are weighted and added to obtain an early-warning score, the early-warning score is used for representing the severity of the failure of the equipment, and the target early-warning data is screened from the initial failure set in combination with the early-warning score, where the target early-warning data is used on the final early-warning prompt.
In some embodiments, step S533 specifically includes the steps of:
s5331, sorting the target weights to obtain a sorting result;
s5332, screening out target early warning data from the initial fault set based on the sorting result, wherein each target early warning data comprises a target weight.
In step S5331, the target weights are ranked to obtain a ranking result, and the user can rank the target weights corresponding to some attributes at the crossing position according to the actual requirement, so that the severity of the ranked high-order fault is relatively high, and the early warning score can also be ranked, so that the severity of the ranked high-order fault is relatively high.
In step S5332, the target early warning data with the ranking result in the front is screened out from the initial failure set.
In step S600, early warning prompt information is generated according to the target early warning data, where the early warning prompt information is used to remind the user of which crossing places may have problems, and which problems exist, and diagnosis and repair are required in time, so as to avoid faults.
In some embodiments, the early warning prompt information includes early warning time and early warning frequency, and correspondingly, step S600 includes determining the early warning time and the early warning frequency according to the size of the target weight, for example, the early warning time can be estimated according to the size of the target weight, that is, when the early warning prompt information is sent to the user, and the early warning frequency, for example, the early warning prompt information is sent to the user several times in a certain time, so as to timely inform and remind the user of the early warning information, thereby improving the user experience.
In practical application, valuable data for state evaluation in mass data are mined through big data analysis, a data fusion algorithm and a data mining technology, a deep learning technology is further utilized, a deep diagnosis model for effectively crossing fault characteristics in the data at the position and accurately identifying multiple faults is provided, a state evaluation model for effectively scoring the fault degree is provided, and the early warning capability of the embodiment of the application can be continuously optimized according to the state evaluation model.
In the embodiment of the application, first initial data of a power grid line to be early-warned are acquired through a laser detector on the unmanned aerial vehicle, wherein the first initial data comprise three-dimensional images of the power grid line; determining a crossing position of the power grid line based on the three-dimensional image, and then collecting second initial data of the crossing position; inputting the second initial data into a preset cross defect diagnosis model, and then screening the second initial data to obtain target early warning data; and generating early warning prompt information according to the target early warning data, and carrying out early warning prompt on the power grid line according to the early warning prompt information. The grid-based cross key part early warning method can automatically diagnose and early warn and analyze the cross point line, improves early warn precision, and can better manage hidden danger risk points in the line.
In a second aspect, embodiments of the present application also provide a grid-based cross-over critical-site warning system for performing the method mentioned in the embodiments of the first aspect.
In some embodiments, as shown in fig. 2, the grid-based cross-key site warning system includes: the system comprises an acquisition module 100, a crossing module 200, an acquisition module 300, an input module 400, a screening module 500 and an early warning module 600, wherein the acquisition module 100 acquires first initial data of a power grid line to be early warned through a laser detector on the unmanned aerial vehicle, and the first initial data comprises a three-dimensional image of the power grid line; the crossing module 200 determines a crossing position of the power grid line based on the three-dimensional image, and the acquisition module 300 acquires second initial data of the crossing position; the input module 400 inputs the second initial data to a preset cross defect diagnosis model, and the screening module 500 screens the second initial data to obtain target early warning data; the early warning module 600 generates early warning prompt information according to the target early warning data, and carries out early warning prompt on the power grid line according to the early warning prompt information. The grid-based crossing key part early warning system can automatically diagnose and early warn and analyze crossing point lines, improves early warn precision, and can better manage hidden danger risk points in the lines.
As shown in fig. 3, another embodiment of a hardware architecture for grid-based cross-over critical-site pre-warning is illustrated, comprising:
the processor 700 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present disclosure;
the memory 800 may be implemented in the form of a ROM (read only memory), a static storage device, a dynamic storage device, or a RAM (random access memory). Memory 800 may store an operating system and other application programs, and when implementing the technical solutions provided in the embodiments of the present disclosure by software or firmware, relevant program codes are stored in memory 800 and invoked by processor 700 to perform the power distribution network fault diagnosis method of the embodiments of the present disclosure;
an input/output interface 900 for implementing information input and output;
the communication interface 1000 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.), or may implement communication in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and
Bus 1100 that transports information between various components of the device (e.g., processor 700, memory 800, input/output interface 900, and communication interface 1000);
wherein the processor 700, the memory 800, the input/output interface 900 and the communication interface 1000 are communicatively coupled to each other within the device via a bus 1100.
In a third aspect, the embodiment of the application also provides a grid-based cross-over critical part early warning system.
In some embodiments, an electronic device includes: at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions that are executed by the at least one processor, so that the at least one processor executes the instructions to implement any grid-based cross-over critical part early warning method in the embodiments of the present application.
The processor and the memory may be connected by a bus or other means.
The memory is used as a non-transitory computer readable storage medium for storing non-transitory software programs and non-transitory computer executable programs, such as the grid-based cross-over critical part early warning method described in the embodiments of the present application. The processor runs a non-transient software program and instructions stored in the memory, so that the grid-based cross-over key part early warning method is realized.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the stored data area may store and perform the grid-based cross-over critical section early warning method described above. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions required to implement the grid-based cross-over critical section warning method described above are stored in memory and when executed by one or more processors, perform the grid-based cross-over critical section warning method mentioned in the embodiments of the first aspect described above.
In a fourth aspect, embodiments of the present application further provide a grid-based cross-over critical location early warning system.
In some embodiments, a computer readable storage medium stores computer executable instructions for performing the grid-based cross-over critical section warning method mentioned in the embodiments of the first aspect.
In some embodiments, the storage medium stores computer-executable instructions that are executed by one or more control processors, e.g., by one of the processors in the electronic device, which may cause the one or more processors to perform the grid-based cross-over critical-site warning method.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The embodiments of the present application have been described in detail above with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application. Furthermore, embodiments of the present application and features of the embodiments may be combined with each other without conflict.
Claims (7)
1. The grid-based crossing key part early warning method is characterized by comprising the following steps of:
acquiring first initial data of a power grid line to be pre-warned through a laser detector on an unmanned aerial vehicle, wherein the first initial data is circuit attribute information of the power grid line, and the circuit attribute information comprises a three-dimensional image, a line name, a voltage value, the number of circuits, the number of circuit splitting, a space configuration structure and an associated position area of the power grid line; the three-dimensional image comprises point cloud data, a digital elevation model, a digital surface model and a digital orthophoto map of a power grid;
determining a crossing of the grid line based on the three-dimensional image;
collecting second initial data of the crossing position, wherein the second initial data is circuit attribute information of the crossing position;
Inputting the second initial data into a preset cross defect diagnosis model, wherein the cross defect diagnosis model comprises a data processing module; the cross-over defect diagnosis model is a deep diagnosis model which is used for excavating valuable data for state evaluation in mass data through big data analysis, a data fusion algorithm and a data mining technology, extracting fault characteristics in the data at the effective cross-over position and accurately identifying multiple faults by further utilizing a deep learning technology;
screening the second initial data according to the data processing module to obtain target early warning data;
generating early warning prompt information according to the target early warning data;
carrying out early warning prompt on the power grid line according to the early warning prompt information;
the obtaining of the first initial data of the power grid line to be pre-warned through the laser detector on the unmanned aerial vehicle comprises the following steps:
determining the position range of a power grid line to be pre-warned;
determining a flight path of the unmanned aerial vehicle according to the position range; the flight route is the route with the shortest flight distance or the route with the widest coverage range;
acquiring a three-dimensional image and position information acquired by a laser detector on the unmanned aerial vehicle based on the flight route; the three-dimensional image is a three-dimensional image of the area to be early-warned; the position information is a position coordinate of the unmanned aerial vehicle in the flight process;
Obtaining the three-dimensional coordinates of the power grid line according to the three-dimensional image and the position information, wherein the three-dimensional coordinates comprise:
marking a three-dimensional coordinate of the power grid line according to the three-dimensional image, and further optimizing the three-dimensional coordinate by combining a position coordinate of the unmanned aerial vehicle in the flying process;
the step of screening the second initial data according to the data processing module to obtain target early warning data comprises the following steps:
determining, by the data processing module, a first fault region at the crossover based on the second initial data, the first fault region including third initial data, the third initial data being circuit attribute information of the first fault region;
constructing an initial fault set according to the third initial data and preset key line information; the initial fault set includes the crossing points where faults may exist and the corresponding causes and information for the faults;
screening the initial fault set to obtain target early warning data, wherein the method comprises the following steps:
acquiring a plurality of initial weights corresponding to a plurality of preset attribute information; the preset attribute information comprises the number of lines at the crossing position, the number of crossing roads and the number of crossing roads;
Determining a plurality of target weights corresponding to a plurality of circuit attribute information in the crossing position according to a plurality of initial weights corresponding to a plurality of preset attribute information;
and screening out target early warning data from the initial fault set according to the target weight corresponding to each attribute information.
2. The grid-based crossover critical section warning method of claim 1, wherein the determining a crossover of the grid line based on the three-dimensional image comprises:
extracting line attribute information of the power grid line from the three-dimensional image;
and acquiring the crossing position of the power grid line according to the line attribute information to obtain the crossing position.
3. The grid-based cross-key location early warning method according to claim 1, wherein the screening the target early warning data from the initial fault set according to the target weight corresponding to each attribute information comprises:
sequencing the target weights to obtain a sequencing result;
and screening out target early warning data from the initial fault set based on the sorting result, wherein each target early warning data comprises the target weight.
4. The grid-based cross-over critical part early warning method according to claim 3, wherein the early warning prompt information comprises early warning time and early warning frequency;
correspondingly, the generating the early warning prompt information according to the target early warning data includes:
and determining early warning time and early warning frequency according to the size of the target weight.
5. The grid-based cross-over critical section early warning system, capable of performing the grid-based cross-over critical section early warning method of claim 1, comprising:
the acquisition module is used for: the acquisition module is used for acquiring first initial data of a power grid line to be pre-warned through a laser detector on the unmanned aerial vehicle, wherein the first initial data is circuit attribute information of the power grid line, and the circuit attribute information comprises a three-dimensional image of the power grid line;
and a crossing module: the crossing module determines a crossing of the grid line based on the three-dimensional image;
and the acquisition module is used for: the acquisition module is used for acquiring second initial data of the crossing part, wherein the second initial data is circuit attribute information of the crossing part;
an input module: the input module is used for inputting the second initial data into a preset cross defect diagnosis model, and the cross defect diagnosis model comprises a data processing module;
And a screening module: the screening module is used for screening the second initial data according to the data processing module to obtain target early warning data;
and the early warning module is used for: the early warning module is used for generating early warning prompt information according to the target early warning data;
and carrying out early warning prompt on the power grid line according to the early warning prompt information.
6. Cross crossing key part early warning system based on electric wire netting, characterized by includes:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions that are executable by the at least one processor to cause the at least one processor to implement the grid-based cross-over critical-site warning method of any of claims 1 to 4 when the instructions are executed.
7. Grid-based crossover critical section warning system, characterized in that it stores computer executable instructions for performing the grid-based crossover critical section warning method according to any of claims 1 to 4.
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