CN117875719B - Substation safety early warning method based on target three-dimensional ranging - Google Patents
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Abstract
The invention discloses a substation safety pre-warning method based on target three-dimensional ranging, which relates to the technical field of data processing, and comprises the following steps: the method comprises the steps of obtaining a substation area image set through three-dimensional ranging monitoring equipment, preprocessing the substation area image set, extracting multidimensional features to obtain a substation image multidimensional feature information set, carrying out feature splicing fusion based on the substation area image multidimensional feature information set, carrying out three-dimensional model reconstruction on spliced and fused substation area panoramic feature fusion information by adopting a triangularization reconstruction algorithm to generate a three-dimensional model of the substation, carrying out dangerous source identification marking on the three-dimensional model, determining target dangerous source anchor frame information to carry out three-dimensional ranging and risk feature assessment, and determining dangerous source early warning grade information to carry out substation safety early warning. The intelligent recognition and early warning of the dangerous sources of the transformer substation are realized by utilizing the three-dimensional ranging, the ineffective early warning triggering is effectively reduced, the recognition accuracy and recognition efficiency of the dangerous sources are improved, and the technical effect of safety early warning timeliness of the transformer substation is further ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a substation safety pre-warning method based on target three-dimensional ranging.
Background
As the scale of the power grid expands, the transformer substation serves as an important node of the power grid, and the safe operation of the transformer substation is crucial for the stability of the whole power grid. However, various dangerous sources exist in the operation process of the transformer substation, such as fire, chemicals, barriers and the like, which can cause potential safety hazards of the operation of the transformer substation, and if the potential hazards are not found and handled in time, serious safety accidents can be caused, so that safety precaution is important for the transformer substation. However, existing transformer substation hazard source identification lacks space ranging information, and identification accuracy is low, so that safety early warning is not timely.
Disclosure of Invention
According to the substation safety early warning method based on the target three-dimensional ranging, the technical problems that in the prior art, substation dangerous source identification lacks space ranging information, the identification accuracy is low, and the safety early warning is not timely are solved, the intelligent substation dangerous source identification early warning is achieved by utilizing the three-dimensional ranging, invalid early warning triggering is effectively reduced, the dangerous source identification accuracy and the identification efficiency are improved, and further the technical effect of substation safety early warning timeliness is guaranteed.
In view of the above problems, the invention provides a substation safety pre-warning method based on target three-dimensional ranging.
In a first aspect, the application provides a substation safety pre-warning method based on target three-dimensional ranging, which comprises the following steps: carrying out multi-view image acquisition on a distribution area of a target substation through three-dimensional ranging monitoring equipment to obtain a substation area image set; preprocessing the substation area image set and extracting multidimensional features to obtain a substation image multidimensional feature information set; feature stitching fusion is carried out based on the multi-dimensional feature information set of the substation images, so that panoramic feature fusion information of the substation area is obtained; performing edge fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation; performing dangerous source identification marking based on the three-dimensional model of the transformer substation, and determining target dangerous source anchor frame information; and carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, determining dangerous source early warning grade information, and carrying out safety early warning on the target transformer substation through the dangerous source early warning grade information.
On the other hand, the application also provides a transformer substation safety pre-warning system based on the target three-dimensional ranging, which comprises: the multi-view image acquisition module is used for acquiring multi-view images of the distribution area of the target substation through the three-dimensional ranging monitoring equipment to acquire a substation area image set; the multi-dimensional feature extraction module is used for preprocessing the substation area image set and extracting multi-dimensional features to obtain a substation image multi-dimensional feature information set; the feature stitching fusion module is used for performing feature stitching fusion based on the multi-dimensional feature information set of the substation image to obtain panoramic feature fusion information of the substation area; the three-dimensional model reconstruction module is used for carrying out marginalization fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation; the dangerous source identification marking module is used for carrying out dangerous source identification marking based on the three-dimensional model of the transformer substation and determining target dangerous source anchor frame information; and the transformer substation safety early warning module is used for carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, determining dangerous source early warning grade information and carrying out safety early warning on the target transformer substation through the dangerous source early warning grade information.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, the computer program when executed by the processor implementing the steps of any of the methods described above.
In a fourth aspect, the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of acquiring a substation area image set through three-dimensional ranging monitoring equipment, preprocessing the substation area image set, extracting multidimensional features to obtain a substation image multidimensional feature information set, and carrying out feature stitching and fusion based on the substation image multidimensional feature information set to obtain substation area panoramic feature fusion information; performing edge fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm, generating a three-dimensional model of the transformer substation, performing dangerous source identification marking, and determining target dangerous source anchor frame information; and carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, and determining a technical scheme of carrying out safety precaution on the target transformer substation by dangerous source precaution grade information. And further, the intelligent recognition and early warning of the dangerous sources of the transformer substation are realized by utilizing three-dimensional ranging, invalid early warning triggering is effectively reduced, the recognition accuracy and recognition efficiency of the dangerous sources are improved, and further, the technical effect of safety early warning timeliness of the transformer substation is ensured.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
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FIG. 1 is a flow diagram of a substation safety pre-warning method based on target three-dimensional ranging;
Fig. 2 is a schematic flow chart of acquiring a substation area image set in the substation safety pre-warning method based on the target three-dimensional ranging;
fig. 3 is a schematic structural diagram of a substation safety warning system based on target three-dimensional ranging;
fig. 4 is a schematic structural view of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a multi-view image acquisition module 11, a multi-dimensional feature extraction module 12, a feature stitching fusion module 13, a three-dimensional model reconstruction module 14, a hazard source identification marking module 15, a substation safety pre-warning module 16, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, those skilled in the art will appreciate that the present application may be embodied as methods, apparatus, electronic devices, and computer-readable storage media. Accordingly, the present application may be embodied in the following forms: complete hardware, complete software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the application may also be embodied in the form of a computer program product in one or more computer-readable storage media, which contain computer program code.
Any combination of one or more computer-readable storage media may be employed by the computer-readable storage media described above. The computer-readable storage medium includes: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium include the following: portable computer magnetic disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disk read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device.
The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws.
The application provides a method, a device and electronic equipment through flow charts and/or block diagrams.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in a computer readable storage medium that can cause a computer or other programmable data processing apparatus to function in a particular manner. Thus, instructions stored in a computer-readable storage medium produce an instruction means which implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application will be described below with reference to the drawings in the present application.
Example 1
As shown in fig. 1, the application provides a substation safety pre-warning method based on target three-dimensional ranging, which comprises the following steps:
Step S1: carrying out multi-view image acquisition on a distribution area of a target substation through three-dimensional ranging monitoring equipment to obtain a substation area image set;
as shown in fig. 2, further, the step of obtaining the substation area image set further includes:
performing application level division on the distribution area of the target transformer substation to obtain application level information of the distribution area of the transformer substation;
Performing equipment monitoring node deployment according to the substation distribution area application level information to obtain substation edge node architecture information;
Carrying out three-dimensional ranging monitoring equipment layout according to the substation edge node architecture information to obtain a three-dimensional ranging edge endpoint network;
And respectively acquiring image data of the distribution area of the target substation based on the three-dimensional ranging edge endpoint network to acquire the substation area image set.
Specifically, in order to realize three-dimensional ranging of a transformer substation and intelligent identification of dangerous sources, multi-view image acquisition is performed on a distribution area of a target transformer substation through three-dimensional ranging monitoring equipment, wherein the three-dimensional ranging monitoring equipment is three-dimensional image intelligent measuring and acquiring equipment, multi-view data can be monitored in a movable mode, and a binocular camera or a multi-view camera is preferred and is used for performing image multi-view acquisition on each equipment area of the target transformer substation to be monitored. The specific data acquisition process comprises the following steps: firstly, carrying out application level division on the distribution areas of the target transformer substation, avoiding incomplete and untimely data acquisition caused by overlarge transformer substation areas, automatically setting application level division rules according to division indexes such as the number of transformer substation equipment, the area occupation area and the like of each distribution area, carrying out level division on each distribution area by applying the level division rules, and correspondingly increasing the application level as the number of the equipment is larger, so as to obtain corresponding application level information of the distribution areas of the transformer substation.
In order to ensure timely acquisition and processing of substation monitoring data, equipment monitoring node deployment is carried out according to the substation distribution area application level information, the number of the equipment monitoring nodes of each substation distribution area is set through the distribution area application level, so that substation edge node architecture information is obtained, and the substation edge node architecture information is the substation area data monitoring processing node distribution information and is used for image data acquisition of the whole coverage substation distribution area. And then, carrying out three-dimensional ranging monitoring equipment layout according to the substation edge node architecture information to obtain a three-dimensional ranging edge endpoint network, wherein the three-dimensional ranging edge endpoint network carries out data acquisition processing on each substation distribution area through the three-dimensional ranging monitoring equipment. And further, respectively carrying out multi-view image data acquisition on the distribution areas of the target transformer substation based on the three-dimensional ranging edge endpoint network to obtain a transformer substation area image set, wherein the transformer substation area image set is image monitoring data containing dangerous source information of each transformer substation distribution area. The comprehensive coverage of the substation image data acquisition and the online monitoring processing of the edge endpoint data are realized, the monitoring white point caused by the picture return mode can be effectively eliminated, and the comprehensive and accurate image data acquisition is improved.
Step S2: preprocessing the substation area image set and extracting multidimensional features to obtain a substation image multidimensional feature information set;
Further, the step of obtaining the multi-dimensional feature information set of the substation image further comprises the following steps:
Filtering pretreatment is carried out on the substation area image set through a Gaussian filter, and a denoising substation area image set is obtained;
carrying out equalization enhancement on the denoising transformer substation area image set based on gamma correction to obtain a standard transformer substation area image set;
Acquiring image description characteristic factors, wherein the image description characteristic factors comprise colors, textures, shapes and spatial relations;
And carrying out multidimensional feature extraction description on the standard substation area image set based on the image description feature factors to obtain the substation image multidimensional feature information set.
Specifically, the device acquisition process may be subject to external interference, which may cause noise to the image. Therefore, in order to ensure the processing quality of the image data, the filtering pretreatment is firstly carried out on the transformer substation area image set through a Gaussian filter, the Gaussian filter is utilized to smoothly reduce noise of the acquired image, the image noise is eliminated, and the quality of the image data is further improved. And carrying out equalization enhancement on the denoised substation area image set based on gamma correction, namely correcting a gamma curve of the image, detecting a dark color part and a light color part in an image signal, increasing the proportion of the dark color part and the light color part, and leading the gray level distribution of the image to be more uniform, thereby improving the contrast effect of the image and obtaining the processed standard substation area image set.
And formulating and acquiring image description characteristic factors which are multidimensional description indexes of image characteristics and comprise colors, textures, shapes, spatial relations and the like. And respectively carrying out multidimensional feature extraction description on each region view image in the standard substation region image set based on the image description feature factors to obtain a corresponding substation image multidimensional feature information set, wherein the substation image multidimensional feature information set is multidimensional description feature information of each region view image. The comprehensive description of the image features is realized, the comprehensiveness of image feature extraction is improved, and the accuracy of the feature fusion of the subsequent panoramic image is further ensured.
Step S3: feature stitching fusion is carried out based on the multi-dimensional feature information set of the substation images, so that panoramic feature fusion information of the substation area is obtained;
further, the step of obtaining panoramic feature fusion information of the transformer substation area further comprises the following steps:
performing image selection on the substation area image set based on the distribution area of the target substation to obtain reference substation area image information and other substation area image information sets;
Performing association mapping on the substation image multi-dimensional feature information set, the reference substation area image information and the rest substation area image information sets to obtain a reference image multi-dimensional feature point set and rest image multi-dimensional feature point sets;
Performing image registration on the reference image multi-dimensional feature point set and the rest image multi-dimensional feature point sets to obtain target region registration image feature information;
and carrying out registration area weighted fusion based on the target area registration image feature information, and splicing to obtain the substation area panoramic feature fusion information.
Specifically, feature stitching fusion is performed based on the multi-dimensional feature information set of the substation images, firstly, image selection is performed on the substation area image set based on the distribution area of the target substation, the front area view angle image of each substation distribution area can be selected as reference substation area image information, and the remaining view angle images of the substation distribution areas are used as remaining substation area image information sets. And performing association mapping on the substation image multidimensional feature information set, the reference substation area image information and the rest substation area image information sets, and performing association mapping matching on the image multidimensional description features and the images with different view angles to obtain a matched reference image multidimensional feature point set and rest image multidimensional feature point sets.
And performing feature similarity calculation on the reference image multidimensional feature point set and the rest image multidimensional feature point sets through a similarity algorithm, and registering images within a preset similarity threshold according to a similarity calculation result to obtain target region registration image feature information, wherein the target region registration image feature information is a registration overlapping image feature set of distribution areas of all substations. And carrying out registration area weighted fusion based on the target area registration image feature information, carrying out multi-scale weighted fusion on the overlapped area features, and obtaining spliced substation area panoramic feature fusion information, wherein the substation area panoramic feature fusion information is panoramic image feature information spliced by the image features with different view angles of each substation distribution area, and a data base is provided for subsequent three-dimensional image modeling. The accuracy of registration and fusion of the multidimensional features of the images is improved, and the feature comprehensiveness of the panoramic images and the accuracy of three-dimensional modeling of the images are ensured.
Step S4: performing edge fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation;
Further, the step of generating the three-dimensional model of the transformer substation further comprises the following steps:
Carrying out planar projection and topological connection on the panoramic feature fusion information of the transformer substation area by adopting a triangularization reconstruction algorithm to obtain a panoramic triangular grid;
performing edge detection based on the panoramic triangular mesh, and extracting to obtain model edge information;
performing feature point selection and model fitting training on the model edge information to determine three-dimensional modeling optimization parameters;
And applying the three-dimensional modeling optimization parameters to the panoramic triangular grid to reconstruct a three-dimensional model, and generating the three-dimensional model of the transformer substation.
Specifically, a triangularization reconstruction algorithm is adopted to carry out two-dimensional plane projection on panoramic feature fusion information of the transformer substation area, and adjacent feature data are connected to form triangular grids, namely panoramic triangular grids, according to topological connection relations of the image feature data. And then, carrying out edge detection on the panoramic triangle mesh by using an edge detection algorithm, extracting characteristic data edge information, wherein the edge refers to an area with intense pixel intensity change in an image, such as contour, texture change and the like, and obtaining corresponding model edge information. Feature key points, such as corner points, inflection points and the like, of the model edge information are selected, and features of the image can be represented. And then, using the selected characteristic key points, performing model fitting training, such as polynomial fitting, spline curve fitting and the like, by using a fitting algorithm, and simultaneously optimizing model parameters in the fitting training process to obtain better fitting effects so as to obtain three-dimensional modeling optimization parameters, wherein the three-dimensional modeling optimization parameters describe information of the shape, the size, the position and the like of the model.
And applying the three-dimensional modeling optimization parameters obtained by fitting training to the panoramic triangle mesh to reconstruct a three-dimensional model, wherein the application method is determined according to specific parameter types and model types. Illustratively, if the parameter is a polynomial coefficient, the coefficient is applied to the vertex coordinates of the mesh, and if the parameter is a control point, the control point is used to adjust the structure of the triangular mesh. After the three-dimensional modeling optimization parameters are applied, the original triangular grid is modified, and model reconstruction is further carried out on the basis of the modified triangular grid, so that a three-dimensional model of the transformer substation is generated, and the three-dimensional model of the transformer substation is used for visually displaying three-dimensional space object distribution information of each distribution area of the transformer substation. The comprehensiveness and the accuracy of the three-dimensional modeling of the image are improved, and the accuracy of the follow-up three-dimensional ranging and the intelligent identification of the dangerous source are further improved.
Step S5: performing dangerous source identification marking based on the three-dimensional model of the transformer substation, and determining target dangerous source anchor frame information;
Specifically, the three-dimensional model of the transformer substation is subjected to dangerous source identification marking through a deep convolutional neural network, non-standard transformer substation operation region characteristics are identified, corresponding target dangerous source anchor frame information is determined through anchor frame marking, and the target dangerous source anchor frame information is marked region information of different dangerous sources and is used for defining and displaying dangerous source position information, so that the accuracy and the ranging efficiency of the follow-up three-dimensional ranging are improved.
Step S6: and carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, determining dangerous source early warning grade information, and carrying out safety early warning on the target transformer substation through the dangerous source early warning grade information.
Further, the step of determining the hazard source early warning level information further includes:
Carrying out three-dimensional ranging identification based on the target dangerous source anchor frame information to obtain dangerous source multi-dimensional ranging information, wherein the dangerous source multi-dimensional ranging information comprises equipment dangerous source distance and dangerous source structure size;
constructing a transformer substation dangerous information base, and carrying out type matching identification based on the dangerous source structure size and the transformer substation dangerous information base to obtain a target dangerous source type;
performing risk influence analysis based on the equipment risk source distance and the target risk source type, and determining a risk source risk influence factor;
and monitoring the risk duration time for acquiring the target risk source anchor frame information, performing gain addition fusion on the risk source risk influence factors based on the risk duration time, and determining the risk source early warning grade information.
Specifically, three-dimensional ranging identification is carried out on the target dangerous source anchor frame information, and dangerous source multi-dimensional ranging information is obtained, wherein the dangerous source multi-dimensional ranging information comprises equipment dangerous source distances, and the equipment dangerous source distances comprise clearance distances, horizontal distances, vertical distances and the like of dangerous source distances from transformer substation equipment; the dangerous source structure size comprises the characteristics of the structural shape, the size and the like of the dangerous source. And constructing a transformer substation hazard information base through big data, wherein the transformer substation hazard information base comprises appearance structure data of various transformer substation hazard sources, such as structural shape and size data of hazard sources of fire, chemicals, barriers and the like. And performing type matching identification based on the dangerous source structure size and the transformer substation dangerous information base to obtain a corresponding target dangerous source type. And performing risk influence analysis based on the equipment risk source distance and the target risk source type, wherein the closer the distance between the risk source and the substation equipment is, the more serious the consequences of the risk source type are, the larger the risk influence is, setting a risk factor evaluation rule through fitting of risk source risk experience data, and performing risk evaluation on the equipment risk source distance and the target risk source type through the risk factor evaluation rule to obtain a risk source distance risk factor and a risk source type risk factor. And then adding the risk source distance risk factor and the risk source type risk factor to be used as a risk source risk influence factor.
And monitoring the risk duration time for acquiring the target dangerous source anchor frame information, wherein the longer the dangerous source duration time is, the larger the risk influence on the substation equipment is. Gain addition fusion is carried out on the risk source risk influence factors based on the risk duration, risk assessment is carried out on the risk duration through a risk factor assessment rule to obtain risk source duration risk factors, product gain calculation is carried out on the risk source risk influence factors based on the risk source duration risk factors, early warning grade division is carried out on calculation results through transformer substation safety management standards, and risk source early warning grade information is determined, wherein the risk source early warning grade information is the early warning degree of the risk source, the higher the grade is, the greater the early warning degree is, and the higher the early warning mode and the processing timeliness are. And carrying out safety precaution on the target transformer substation through the dangerous source precaution grade information so as to facilitate operation and maintenance personnel to timely process the dangerous source of the transformer substation. The intelligent recognition and early warning of the dangerous sources of the transformer substation are realized by utilizing the three-dimensional ranging, the ineffective early warning triggering is effectively reduced, the recognition accuracy and recognition efficiency of the dangerous sources are improved, and further the safety early warning timeliness of the transformer substation is ensured.
Further, the steps of the application also comprise:
Determining a three-dimensional ranging updating period according to the safety management standard of the transformer substation;
Image acquisition and comparison are carried out on the target transformer substation according to the three-dimensional ranging updating period, and updated ranging data information of the transformer substation is obtained;
and based on the updated ranging data information of the transformer substation, performing ranging optimization updating on the three-dimensional model of the transformer substation.
Specifically, in order to ensure timely identification and early warning of the dangerous source, a three-dimensional ranging update period is determined according to a substation safety management standard, wherein the three-dimensional ranging update period is an interval update period for image data acquisition ranging of a substation distribution area, and the three-dimensional ranging update period of an area with more occurrence frequency of the dangerous source can be set smaller so as to update the ranging information of the dangerous source timely. And carrying out image acquisition and comparison on the target transformer substation according to the three-dimensional ranging updating period, and taking different comparison data as transformer substation updating ranging data information which is updated image data of the next acquisition ranging period. Based on the distance measurement data information updated by the transformer substation, the three-dimensional model of the transformer substation is subjected to distance measurement optimization updating, and timely updating of the dangerous source distance measurement information is realized through periodic updating of the distance measurement data of the transformer substation, so that safety pre-warning timeliness of the transformer substation is ensured.
In summary, the substation safety pre-warning method based on the target three-dimensional ranging provided by the application has the following technical effects:
The method comprises the steps of acquiring a substation area image set through three-dimensional ranging monitoring equipment, preprocessing the substation area image set, extracting multidimensional features to obtain a substation image multidimensional feature information set, and carrying out feature stitching and fusion based on the substation image multidimensional feature information set to obtain substation area panoramic feature fusion information; performing edge fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm, generating a three-dimensional model of the transformer substation, performing dangerous source identification marking, and determining target dangerous source anchor frame information; and carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, and determining a technical scheme of carrying out safety precaution on the target transformer substation by dangerous source precaution grade information. And further, the intelligent recognition and early warning of the dangerous sources of the transformer substation are realized by utilizing three-dimensional ranging, invalid early warning triggering is effectively reduced, the recognition accuracy and recognition efficiency of the dangerous sources are improved, and further, the technical effect of safety early warning timeliness of the transformer substation is ensured.
Example two
Based on the same inventive concept as the substation safety early warning method based on the target three-dimensional ranging in the foregoing embodiment, the present invention further provides a substation safety early warning system based on the target three-dimensional ranging, as shown in fig. 3, where the system includes:
the multi-view image acquisition module 11 is used for acquiring multi-view images of the distribution area of the target substation through the three-dimensional ranging monitoring equipment to acquire a substation area image set;
The multidimensional feature extraction module 12 is used for preprocessing the substation area image set and extracting multidimensional features to obtain a substation image multidimensional feature information set;
The feature stitching fusion module 13 is used for performing feature stitching fusion based on the multi-dimensional feature information set of the substation image to obtain panoramic feature fusion information of the substation area;
The three-dimensional model reconstruction module 14 is used for carrying out marginalization fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation;
the dangerous source identification marking module 15 is used for carrying out dangerous source identification marking based on the three-dimensional model of the transformer substation and determining target dangerous source anchor frame information;
and the transformer substation safety pre-warning module 16 is used for carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, determining dangerous source pre-warning grade information and carrying out safety pre-warning on the target transformer substation through the dangerous source pre-warning grade information.
Further, the system further comprises:
The application level dividing unit is used for carrying out application level division on the distribution area of the target transformer substation and obtaining application level information of the distribution area of the transformer substation;
The monitoring node deployment unit is used for deploying equipment monitoring nodes according to the application level information of the substation distribution area and acquiring substation edge node architecture information;
the monitoring equipment layout unit is used for carrying out three-dimensional ranging monitoring equipment layout according to the substation edge node architecture information to obtain a three-dimensional ranging edge endpoint network;
And the image data acquisition unit is used for respectively carrying out image data acquisition on the distribution areas of the target transformer substation based on the three-dimensional ranging edge endpoint network to acquire the transformer substation area image set.
Further, the system further comprises:
The filtering preprocessing unit is used for carrying out filtering preprocessing on the substation area image set through a Gaussian filter to obtain a denoised substation area image set;
The equalization enhancement unit is used for performing equalization enhancement on the denoising transformer substation area image set based on gamma correction to obtain a standard transformer substation area image set;
A descriptive feature factor obtaining unit, configured to obtain an image descriptive feature factor, where the image descriptive feature factor includes a color, a texture, a shape, and a spatial relationship;
And the multidimensional feature extraction description unit is used for carrying out multidimensional feature extraction description on the standard substation area image set based on the image description feature factors to obtain the substation image multidimensional feature information set.
Further, the system further comprises:
The image selection unit is used for performing image selection on the substation area image set based on the distribution area of the target substation to obtain reference substation area image information and other substation area image information sets;
The feature association mapping unit is used for carrying out association mapping on the substation image multidimensional feature information set, the reference substation area image information and the rest substation area image information sets to obtain a reference image multidimensional feature point set and rest image multidimensional feature point sets;
The image registration unit is used for carrying out image registration on the reference image multidimensional feature point set and the rest image multidimensional feature point sets to obtain target region registration image feature information;
And the registration weighted fusion unit is used for carrying out registration region weighted fusion based on the target region registration image feature information and splicing to obtain the substation region panoramic feature fusion information.
Further, the system further comprises:
the triangular grid obtaining unit is used for carrying out planar projection and topological connection on the panoramic characteristic fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to obtain a panoramic triangular grid;
The edge detection unit is used for carrying out edge detection based on the panoramic triangle mesh and extracting to obtain model edge information;
The model fitting training unit is used for selecting characteristic points and performing model fitting training on the model edge information to determine three-dimensional modeling optimization parameters;
And the three-dimensional model reconstruction unit is used for applying the three-dimensional modeling optimization parameters to the panoramic triangle mesh to reconstruct the three-dimensional model and generating the three-dimensional model of the transformer substation.
Further, the system further comprises:
The three-dimensional ranging identification unit is used for carrying out three-dimensional ranging identification based on the target dangerous source anchor frame information to obtain dangerous source multi-dimensional ranging information, wherein the dangerous source multi-dimensional ranging information comprises equipment dangerous source distance and dangerous source structure size;
The type matching identification unit is used for constructing a transformer substation dangerous information base, and carrying out type matching identification on the basis of the structural size of the dangerous source and the transformer substation dangerous information base to obtain a target dangerous source type;
The risk influence analysis unit is used for carrying out risk influence analysis based on the equipment risk source distance and the target risk source type and determining risk source risk influence factors;
The gain addition fusion unit is used for monitoring and obtaining the risk duration of the target dangerous source anchor frame information, carrying out gain addition fusion on the dangerous source risk influence factors based on the risk duration, and determining the dangerous source early warning grade information.
Further, the system further comprises:
The updating period determining unit is used for determining a three-dimensional ranging updating period according to the safety management standard of the transformer substation;
The image acquisition and comparison unit is used for carrying out image acquisition and comparison on the target transformer substation according to the three-dimensional ranging updating period to obtain updated ranging data information of the transformer substation;
and the ranging optimization updating unit is used for updating the ranging optimization of the three-dimensional model of the transformer substation based on the updated ranging data information of the transformer substation.
The various variations and specific examples of the substation security early warning method based on the target three-dimensional ranging in the first embodiment of fig. 1 are applicable to the substation security early warning system based on the target three-dimensional ranging in this embodiment, and by the foregoing detailed description of the substation security early warning method based on the target three-dimensional ranging, those skilled in the art can clearly know the implementation method of the substation security early warning system based on the target three-dimensional ranging in this embodiment, so that, for brevity of the description, no detailed description will be given here.
In addition, the application also provides an electronic device, which comprises a bus, a transceiver, a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are respectively connected through the bus, and when the computer program is executed by the processor, the processes of the method embodiment for controlling output data are realized, and the same technical effects can be achieved, so that repetition is avoided and redundant description is omitted.
Exemplary electronic device
In particular, referring to FIG. 4, the present application also provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In the present application, the electronic device further includes: computer programs stored on the memory 1150 and executable on the processor 1120, which when executed by the processor 1120, implement the various processes of the method embodiments described above for controlling output data.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In the present application, bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits, including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such an architecture includes: industry standard architecture buses, micro-channel architecture buses, expansion buses, video electronics standards association, and peripheral component interconnect buses.
Processor 1120 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by instructions in the form of integrated logic circuits in hardware or software in a processor. The processor includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic blocks disclosed in the present application may be implemented or performed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated on a single chip or located on multiple different chips.
The processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules in a decoding processor. The software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as known in the art. The readable storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
Bus 1110 may also connect together various other circuits such as peripheral devices, voltage regulators, or power management circuits, bus interface 1140 providing an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, the present application will not be further described.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 is configured to transmit the data processed by the processor 1120 to the other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, for example: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It should be appreciated that in the present application, the memory 1150 may further include memory located remotely from the processor 1120, which may be connected to a server through a network. One or more portions of the above-described networks may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, an internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and combinations of two or more of the foregoing. For example, the cellular telephone network and wireless network may be global system for mobile communications devices, code division multiple access devices, worldwide interoperability for microwave access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, advanced long term evolution devices, general mobile communications devices, enhanced mobile broadband devices, mass machine class communications devices, ultra-reliable low-latency communications devices, and the like.
It should be appreciated that the memory 1150 in the present application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which serves as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the memory described above and any other suitable type of memory.
In the present application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an extended set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The applications 1152 include various applications such as: and the media player and the browser are used for realizing various application services. A program for implementing the method of the present application may be included in the application 1152. The application 1152 includes: applets, objects, components, logic, data structures, and other computer apparatus-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the application also provides a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above-mentioned method embodiment for controlling output data, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (9)
1. The substation safety early warning method based on the target three-dimensional ranging is characterized by comprising the following steps of:
Carrying out multi-view image acquisition on a distribution area of a target substation through three-dimensional ranging monitoring equipment to obtain a substation area image set;
Preprocessing the substation area image set and extracting multidimensional features to obtain a substation image multidimensional feature information set;
feature stitching fusion is carried out based on the multi-dimensional feature information set of the substation images, so that panoramic feature fusion information of the substation area is obtained;
performing edge fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation;
performing dangerous source identification marking based on the three-dimensional model of the transformer substation, and determining target dangerous source anchor frame information;
Three-dimensional ranging and risk characteristic evaluation are carried out on the target dangerous source anchor frame information, dangerous source early warning grade information is determined, and safety early warning is carried out on the target transformer substation through the dangerous source early warning grade information;
The obtaining the substation image multidimensional feature information set comprises the following steps:
Filtering pretreatment is carried out on the substation area image set through a Gaussian filter, and a denoising substation area image set is obtained;
carrying out equalization enhancement on the denoising transformer substation area image set based on gamma correction to obtain a standard transformer substation area image set;
Acquiring image description characteristic factors, wherein the image description characteristic factors comprise colors, textures, shapes and spatial relations;
And carrying out multidimensional feature extraction description on the standard substation area image set based on the image description feature factors to obtain the substation image multidimensional feature information set.
2. The method of claim 1, wherein the acquiring a substation area image set comprises:
performing application level division on the distribution area of the target transformer substation to obtain application level information of the distribution area of the transformer substation;
Performing equipment monitoring node deployment according to the substation distribution area application level information to obtain substation edge node architecture information;
Carrying out three-dimensional ranging monitoring equipment layout according to the substation edge node architecture information to obtain a three-dimensional ranging edge endpoint network;
And respectively acquiring image data of the distribution area of the target substation based on the three-dimensional ranging edge endpoint network to acquire the substation area image set.
3. The method of claim 1, wherein the obtaining substation area panorama feature fusion information comprises:
performing image selection on the substation area image set based on the distribution area of the target substation to obtain reference substation area image information and other substation area image information sets;
Performing association mapping on the substation image multi-dimensional feature information set, the reference substation area image information and the rest substation area image information sets to obtain a reference image multi-dimensional feature point set and rest image multi-dimensional feature point sets;
Performing image registration on the reference image multi-dimensional feature point set and the rest image multi-dimensional feature point sets to obtain target region registration image feature information;
and carrying out registration area weighted fusion based on the target area registration image feature information, and splicing to obtain the substation area panoramic feature fusion information.
4. The method of claim 1, wherein the generating a three-dimensional stereoscopic model of the substation comprises:
Carrying out planar projection and topological connection on the panoramic feature fusion information of the transformer substation area by adopting a triangularization reconstruction algorithm to obtain a panoramic triangular grid;
performing edge detection based on the panoramic triangular mesh, and extracting to obtain model edge information;
performing feature point selection and model fitting training on the model edge information to determine three-dimensional modeling optimization parameters;
And applying the three-dimensional modeling optimization parameters to the panoramic triangular grid to reconstruct a three-dimensional model, and generating the three-dimensional model of the transformer substation.
5. The method of claim 1, wherein the determining hazard source pre-warning level information comprises:
Carrying out three-dimensional ranging identification based on the target dangerous source anchor frame information to obtain dangerous source multi-dimensional ranging information, wherein the dangerous source multi-dimensional ranging information comprises equipment dangerous source distance and dangerous source structure size;
constructing a transformer substation dangerous information base, and carrying out type matching identification based on the dangerous source structure size and the transformer substation dangerous information base to obtain a target dangerous source type;
performing risk influence analysis based on the equipment risk source distance and the target risk source type, and determining a risk source risk influence factor;
and monitoring the risk duration time for acquiring the target risk source anchor frame information, performing gain addition fusion on the risk source risk influence factors based on the risk duration time, and determining the risk source early warning grade information.
6. The method of claim 1, wherein the method comprises:
Determining a three-dimensional ranging updating period according to the safety management standard of the transformer substation;
Image acquisition and comparison are carried out on the target transformer substation according to the three-dimensional ranging updating period, and updated ranging data information of the transformer substation is obtained;
and based on the updated ranging data information of the transformer substation, performing ranging optimization updating on the three-dimensional model of the transformer substation.
7. Substation safety early warning system based on target three-dimensional ranging, which is characterized in that the system comprises:
The multi-view image acquisition module is used for acquiring multi-view images of the distribution area of the target substation through the three-dimensional ranging monitoring equipment to acquire a substation area image set;
The multi-dimensional feature extraction module is used for preprocessing the substation area image set and extracting multi-dimensional features to obtain a substation image multi-dimensional feature information set;
The feature stitching fusion module is used for performing feature stitching fusion based on the multi-dimensional feature information set of the substation image to obtain panoramic feature fusion information of the substation area;
The three-dimensional model reconstruction module is used for carrying out marginalization fitting training and three-dimensional model reconstruction on the panoramic feature fusion information of the transformer substation area by adopting a triangulation reconstruction algorithm to generate a three-dimensional model of the transformer substation;
The dangerous source identification marking module is used for carrying out dangerous source identification marking based on the three-dimensional model of the transformer substation and determining target dangerous source anchor frame information;
The transformer substation safety early warning module is used for carrying out three-dimensional ranging and risk characteristic evaluation on the target dangerous source anchor frame information, determining dangerous source early warning grade information and carrying out safety early warning on the target transformer substation through the dangerous source early warning grade information;
The filtering preprocessing unit is used for carrying out filtering preprocessing on the substation area image set through a Gaussian filter to obtain a denoised substation area image set;
The equalization enhancement unit is used for performing equalization enhancement on the denoising transformer substation area image set based on gamma correction to obtain a standard transformer substation area image set;
A descriptive feature factor obtaining unit, configured to obtain an image descriptive feature factor, where the image descriptive feature factor includes a color, a texture, a shape, and a spatial relationship;
And the multidimensional feature extraction description unit is used for carrying out multidimensional feature extraction description on the standard substation area image set based on the image description feature factors to obtain the substation image multidimensional feature information set.
8. An electronic device comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program when executed by the processor implements the steps in the substation safety warning method based on target three-dimensional ranging according to any one of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps in the substation safety pre-warning method based on target three-dimensional ranging according to any one of claims 1-6.
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| CN118447158A (en) * | 2024-04-26 | 2024-08-06 | 江苏濠汉信息技术有限公司 | Three-dimensional reconstruction and measurement method and system utilizing multi-eye stereo vision |
| CN118134267B (en) * | 2024-05-08 | 2024-08-30 | 江苏濠汉信息技术有限公司 | Substation safety early warning method and system based on multi-data fusion modeling |
| CN118350652B (en) * | 2024-06-18 | 2024-08-30 | 江苏濠汉信息技术有限公司 | Safety protection method and system for substation operators |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097648A (en) * | 2016-08-04 | 2016-11-09 | 中国南方电网有限责任公司超高压输电公司贵阳局 | Transformer station's fortune inspection working space three-dimensional security monitoring method |
| WO2022036866A1 (en) * | 2020-08-21 | 2022-02-24 | 广东电网有限责任公司清远供电局 | Error-prevention and early-warning system for transformer substation operation |
| CN115597659A (en) * | 2022-09-21 | 2023-01-13 | 山东锐翊电力工程有限公司（Cn） | Intelligent safety management and control method for transformer substation |
| CN116883999A (en) * | 2023-06-29 | 2023-10-13 | 西安理工大学 | Safety distance monitoring method for large-scale construction machinery and working area of transformer substation |
-
2024
- 2024-03-11 CN CN202410268523.8A patent/CN117875719B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106097648A (en) * | 2016-08-04 | 2016-11-09 | 中国南方电网有限责任公司超高压输电公司贵阳局 | Transformer station's fortune inspection working space three-dimensional security monitoring method |
| WO2022036866A1 (en) * | 2020-08-21 | 2022-02-24 | 广东电网有限责任公司清远供电局 | Error-prevention and early-warning system for transformer substation operation |
| CN115597659A (en) * | 2022-09-21 | 2023-01-13 | 山东锐翊电力工程有限公司（Cn） | Intelligent safety management and control method for transformer substation |
| CN116883999A (en) * | 2023-06-29 | 2023-10-13 | 西安理工大学 | Safety distance monitoring method for large-scale construction machinery and working area of transformer substation |
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