CN111831856A - Metadata-based automatic holographic digital power grid data storage system and method - Google Patents
Metadata-based automatic holographic digital power grid data storage system and method Download PDFInfo
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Abstract
The invention provides a holographic digital power grid data automatic storage system based on metadata, which comprises: the system comprises a data acquisition module, a data primary check module, a data uploading module and a data analysis and import module, wherein the data acquisition module is used for acquiring professional remote sensing data of power transmission, power transformation and power distribution to be warehoused, determining metadata corresponding to the remote sensing data and an original complete marking value of the metadata, the data primary check module is used for judging the format of the remote sensing data and calculating and determining the integrity of the metadata, the data uploading module is used for encrypting and uploading data meeting requirements, the metadata analysis and import module is used for analyzing the metadata of the uploaded remote sensing data and automatically warehousing the remote sensing data and the metadata according to metadata information. By adopting the system, the intelligent automatic warehousing of the professional remote sensing data and the corresponding metadata of power transmission, power transformation and power distribution can be realized, the defects of time and labor waste, high error rate and poor confidentiality in the prior warehousing technology are overcome, the accuracy of data transmission and warehousing results is greatly improved, and the reasonable utilization of the storage space of a database is facilitated.
Description
Technical Field
The invention relates to the technical field of remote sensing data processing, in particular to a metadata-based automatic holographic digital power grid data storage system and a metadata-based automatic holographic digital power grid data storage method.
Background
The rapid development of the power industry drives the rise of monitoring requirements, in order to meet the monitoring requirements of the power industry and ensure the safe operation of a power system, the introduction of intelligent equipment for automatic shooting and monitoring is one of efficient and reasonable solutions, for example, an unmanned aerial vehicle with a shooting device is adopted for automatic shooting and monitoring of power lines and equipment in real time. Therefore, in the current monitoring process of the power industry, massive remote sensing data can be generated and needs to be processed and transmitted.
In the prior art, local remote sensing data is generally copied or transmitted to a database server through a mobile storage medium or a network, then the data is manually imported into a database on the data server, metadata is added one by one, and finally the validity of the data is manually checked, but in the process of data transmission and storage, all work needs manual operation, images need to be manually sorted and classified, the automation and intelligence degrees are low, the workload is huge, time and labor are wasted, the error rate is high due to the fact that the mass characteristics of the remote sensing data are obtained through monitoring, and meanwhile, due to the fact that personnel are involved are complicated, the confidentiality and the safety of the data cannot be well guaranteed; due to the characteristics of unique diversity format and large unit data capacity of remote sensing data, particularly laser point cloud data, the sampling density is high, the point cloud distribution is dense, the point cloud data which is not classified and unorganized is not beneficial to the three-dimensional application of the point cloud data, the traditional data storage method inevitably needs to realize data auditing and data classification operation in advance by means of manual processing, time and labor are consumed, the problem of data omission easily occurs in the data storage process, and efficient and reliable automatic storage operation cannot be realized for the remote sensing data.
Disclosure of Invention
In order to solve the above problems, the present invention provides a metadata-based holographic digital grid data automatic warehousing system, which in one embodiment comprises:
the system comprises a data acquisition module, a storage module and a storage module, wherein the data acquisition module is configured to acquire professional remote sensing data of power transmission, power transformation and power distribution with storage requirements in a power grid, and determine metadata corresponding to the remote sensing data and an original complete mark value of the metadata, wherein the remote sensing data comprises visible light photographic data and laser point cloud data;
the data initial check module is configured to judge whether the format of the obtained remote sensing data is correct or not, calculate and determine the integrity of metadata corresponding to the remote sensing data, and determine that the remote sensing data and the corresponding metadata meet requirements when the format of the remote sensing data is correct and the corresponding metadata are complete;
the data uploading module is configured to encrypt and upload the remote sensing data meeting the requirements and the corresponding metadata to a server based on a set transmission protocol; the transmission protocol is set according to the data format, the data wave band, the data time phase and the resolution of the current remote sensing data;
and the data analysis import module is configured to analyze the uploaded metadata of the remote sensing data, acquire corresponding metadata information, and automatically import the remote sensing data and the metadata into a database according to the metadata information.
In one embodiment, the data acquisition module comprises:
the remote sensing data acquisition unit is configured to call corresponding remote sensing data from a remote sensing data acquisition equipment terminal or receive remote sensing data uploaded by a user according to the warehousing requirement of the user;
the remote sensing data classification unit is configured to divide visible light shooting data and laser point cloud data according to a data format of the remote sensing data which is obtained, and automatically classify the laser point cloud data by adopting a pre-constructed classification model;
the metadata acquisition unit is configured to describe the visible light photographic data and the automatically classified laser point cloud data, acquire corresponding metadata and generate an original complete mark value representing the integrity of the metadata;
the classification model is obtained by training based on a screened training sample and a deep learning strategy.
In one embodiment, the data acquisition module trains the classification model by:
screening a set number of manual training samples from the existing laser point cloud data samples by workers considering different line types and different tower types;
determining ground points in a training sample by using a filtering algorithm, and further manually dividing sample data except the ground points in the training sample into tower data, power line data and other types of data, wherein the other types of data represent other point cloud data except the ground points, the tower data and the power line data;
selecting neighborhood points of each point, respectively calculating data characteristics of power line data and tower data, and constructing a classifier according to the data characteristics;
and automatically classifying other samples except the manual training sample in the laser point cloud data sample in batches by using the classifier, performing speckle combination on the automatic classification result to realize optimization of the classifier until the classification precision of the classifier meets a set condition, and taking the obtained classifier as a classification model.
In one embodiment, the remote sensing data classification unit is further configured to:
in the process of automatically classifying the laser point cloud data by adopting a pre-constructed classification model, the automatic classification effect is checked, the manual interaction mode is adopted for editing, and the misclassified or misclassified area is corrected to generate a new laser point cloud data sample for further optimization of the classification model.
In one embodiment, the metadata obtaining unit is further configured to:
and describing the laser point cloud data according to the power transmission equipment management ledger information, acquiring corresponding metadata, generating an XML file of the metadata, and organizing and processing the XML file by using a tree structure.
In one embodiment, the data acquisition module calculates the data characteristics of the power line data and tower data by:
for power line data, calculating data characteristics of cloud data of each point by adopting a Hough linear detection algorithm; for tower data, firstly, corrosion operation is adopted to eliminate the power line boundary, and then expansion operation is utilized to obtain the communication area of the tower gathering point group.
In one embodiment, the data primary check module is further configured to:
and judging whether the format of the remote sensing data is correct or not according to the actual requirements of the application of the remote sensing data and the requirements of the design of a database, and determining the file format of the metadata information stored in the database by the remote sensing data.
In one embodiment, the data primary check module is further configured to:
and calculating a first integrity representation value of the metadata corresponding to the obtained remote sensing data, and if the first integrity representation value is equal to the original integrity marking value of the metadata, judging that the obtained metadata is complete.
In one embodiment, the system further comprises:
the data rechecking module is configured to check the integrity of metadata corresponding to the uploaded remote sensing data, if the metadata are incomplete, the check result is fed back to the data uploading module, and the metadata are uploaded again by the data uploading module;
the data rechecking module checks the integrity of metadata corresponding to the uploaded remote sensing data through the following steps:
and calculating a second integrity characteristic value of the metadata corresponding to the uploaded remote sensing data, comparing the second integrity characteristic value with an original integrity marking value or a first integrity characteristic value corresponding to the metadata, and if the second integrity characteristic value is equal to any one of the first integrity characteristic values, indicating that the transmitted metadata is complete.
In one embodiment, the data parsing import module automatically imports the telemetry data and the metadata into the database by performing the following operations:
and determining a storage path of the remote sensing data and the metadata according to the metadata information obtained by analysis, combining the storage path and the metadata to be used as new metadata, storing the new metadata and the corresponding remote sensing data and the metadata information into a database, and returning a processing result and prompt information to a user after the storage is finished.
In one embodiment, the data parsing import module determines a storage path of the telemetric data and corresponding metadata by:
analyzing a file stored with metadata to obtain metadata information, selecting a table with fields in a database consistent with the obtained metadata information fields as a storage path of the current metadata, and further determining the storage position of the remote sensing data according to the naming information of the remote sensing data in the metadata, wherein the fields in the metadata information are determined according to the actual working condition requirements of the corresponding remote sensing data, and the naming information of the remote sensing data is set according to a set rule based on the metadata information.
Based on other aspects of any one or more of the above embodiments, the present invention further provides a metadata-based method for automatically warehousing remote sensing data, where the method is applied to the system in any one or more of the above embodiments.
Compared with the closest prior art, the invention also has the following beneficial effects:
according to the automatic holographic digital power grid data storage system based on the metadata, after the data acquisition module acquires the remote sensing data, the data acquisition module further determines the metadata respectively aiming at the visible light shooting data and the laser point cloud data through a specific scheme, so that the original complete identification value of the metadata is determined, and a foundation is laid for judging the integrity of the acquired data; the data primary inspection module of the system verifies the format of the acquired remote sensing data and the integrity of corresponding metadata before uploading the data, and uploads the acquired data after determining that the acquired data meets requirements, so that data errors caused by data damage in the acquisition process are avoided, and the data accuracy is guaranteed; in addition, the data analysis import module of the system automatically stores all data according to the metadata information obtained by analyzing the uploaded metadata, automatically associates the remote sensing data and the metadata stored in the database on the basis of quickly realizing automatic data storage, is favorable for searching and further analyzing of a user, and simultaneously improves the reasonable utilization rate of the storage space in the database.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of an automatic holographic digital power grid data warehousing system based on metadata according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an operating principle of a metadata-based holographic digital power grid data automatic storage system in an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an automatic holographic digital power grid data warehousing system based on metadata according to another embodiment of the present invention;
fig. 4 is a schematic flow chart of a method for automatically warehousing remote sensing data based on metadata according to an embodiment of the present invention.
Detailed Description
The following detailed description will be provided for the embodiments of the present invention with reference to the accompanying drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the technical effects, and implement the present invention according to the implementation procedures. It should be noted that, unless otherwise conflicting, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are all within the scope of the present invention.
Remote sensing is an emerging technology developed in the early 60's of the 20 th century based on aerial photography. Remote sensing data is used as an ultra-high capacity information carrier, and has been widely applied in the fields of resource investigation, disaster monitoring, marine fishery, geological prospecting and the like because the remote sensing data can provide various information timely, reliably and conveniently. With the continuous development of aerospace technologies and sensor technologies, the remote sensing data acquisition technology tends to be three-more (multi-platform, multi-sensor and multi-angle) and three-high (high spatial resolution, high spectral resolution and high time resolution), so that the remote sensing data volume in different fields is increased at a remarkable speed.
The rapid development of the power industry drives the rise of monitoring requirements, in order to meet the increasingly high monitoring requirements of the power industry and ensure the safe operation of a power system, the introduction of intelligent equipment to realize automatic shooting and monitoring is one of efficient and reasonable solutions, for example, an unmanned aerial vehicle with a shooting device is adopted to automatically shoot and monitor power lines and equipment in real time. Therefore, in the current monitoring process of the power industry, massive remote sensing data can be generated and needs to be processed and transmitted, and the data is usually remote sensing image data obtained by shooting. In practical application, people can acquire various kinds of observation image data of the ground in real time by utilizing equipment such as an unmanned aerial vehicle and a sensor, and the equipment has the characteristics of multiple bands, multiple time phases and multiple resolutions. Due to the diversity of data acquisition ways, the remote sensing image shows the increase of geometric progression in magnitude, the initial GB increases to TB level and even PB level, and in the face of the increase speed of the magnitude, the problems of rapid storage and efficient management of the remote sensing image are solved by using a new technical scheme, and the method is also the application direction at the present stage.
The remote sensing data of the national power grid company has the characteristics of being different from general data in terms of the mass property, the confidentiality and the like, so that the requirement on automatic data processing is higher, and a series of problems of remote sensing data processing, storage, management and the like need to be overcome. The warehousing of the remote sensing data is taken as the daily work of data processing personnel, the automation degree is low at present, and the remote sensing data is a weak link in the application of the remote sensing data. The functions of automatic association, automatic warehousing, automatic naming and the like of the remote sensing data are realized, and the method has important significance for shortening the processing time of the remote sensing data and expanding the application of the remote sensing data.
In the prior art, local remote sensing data is generally copied or transmitted to a database server through a mobile storage medium or a network, then the data is manually imported into a database on the database server, metadata is added one by one, and finally the validity of the data is manually checked.
However, the above technical solution has the following drawbacks:
(1) in the data transmission and storage process, each work needs manual operation, the workload is huge, and time and labor are wasted;
(2) the error rate of manual import is high due to the fact that massive characteristics of remote sensing data are obtained through monitoring;
(3) the data processing process involves complicated personnel and cannot well guarantee the confidentiality and the safety of the data.
Furthermore, it should be noted that, due to the characteristic of the remote sensing data having a unique diversity format and a large unit data capacity, especially laser point cloud data, the sampling density is high, the point cloud distribution is dense, the unsorted and unorganized point cloud data is not favorable for the three-dimensional application of the point cloud data, the conventional data storage method inevitably needs to implement data auditing and data classifying operation in advance by means of manual processing, and is time-consuming and labor-consuming, and the problem of data omission easily occurs in the data storage process, and the efficient and reliable automatic storage operation cannot be implemented for the remote sensing data.
In order to solve the problems, the invention provides a metadata-based holographic digital power grid data automatic storage system and a metadata-based holographic digital power grid data automatic storage method, which deeply analyze and transform the data storage process of the traditional technology from the characteristics of remote sensing data and the actual requirements of data processing personnel, and provide a metadata-based remote sensing data automatic storage implementation scheme, which achieves the purpose of the invention based on the metadata characteristics, the advantages of XML files, the data transmission requirements, MD5 calculation and other key technologies. Various embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Fig. 1 shows a schematic structural diagram of a metadata-based holographic digital power grid data automatic warehousing system according to an embodiment of the present invention, and as can be seen from fig. 1, the system includes: the device comprises a data acquisition module, a data initial check module, a data uploading module and a data analysis and import module.
The data acquisition module 11 is configured to acquire professional remote sensing data of power transmission, power transformation and power distribution with warehousing requirements in a power grid, and determine metadata corresponding to the remote sensing data and an original complete mark value of the metadata, wherein the remote sensing data comprises visible light shooting data and laser point cloud data.
Specifically, in an embodiment, the data obtaining module includes:
and the remote sensing data acquisition unit is configured to call corresponding remote sensing data from the remote sensing data acquisition equipment terminal or receive remote sensing data uploaded by a user according to the warehousing requirements of the user. In practical application, for example, the remote sensing data to be put in storage is called from a shooting device or a laser radar carried by an aerial unmanned aerial vehicle for collecting the remote sensing data, or the remote sensing data to be put in storage uploaded by a user is received.
The remote sensing data classification unit is configured to divide visible light shooting data and laser point cloud data according to a data format of the remote sensing data which is obtained, and automatically classify the laser point cloud data by adopting a pre-constructed classification model.
And the metadata acquisition unit is configured to describe the visible light shooting data and the automatically classified laser point cloud data, acquire corresponding metadata and generate an original complete mark value representing the integrity of the metadata.
The classification model is obtained by training based on a screened training sample and a deep learning strategy.
Further, the data acquisition module trains the classification model by:
screening a set number of manual training samples from the existing laser point cloud data samples by workers considering different line types and different tower types;
determining ground points in a training sample by using a filtering algorithm, and further manually dividing sample data except the ground points in the training sample into tower data, power line data and other types of data, wherein the other types of data represent other point cloud data except the ground points, the tower data and the power line data;
and based on the result of the first-step manual labeling, performing feature calculation and extraction on the result of the manual labeling by using an algorithm. Therefore, there are: selecting neighborhood points of each point, respectively calculating data characteristics of power line data and tower data, and constructing a classifier according to the data characteristics; specifically, in an embodiment, the data obtaining module calculates the data characteristics of the power line data and the tower data respectively by:
for power line data, calculating data characteristics of cloud data of each point by adopting a Hough linear detection algorithm; for tower data, firstly, corrosion operation is adopted to eliminate the power line boundary, and then expansion operation is utilized to obtain the communication area of the tower gathering point group.
And automatically classifying other samples except the manual training samples in the laser point cloud data samples in batches by using the classifier, performing speckle combination on the automatic classification results to optimize the classifier until the classification precision of the classifier meets a set condition, and taking the obtained classifier as a classification model.
In practical application, the laser point cloud data is a basic data source for visualization of a power transmission line corridor, the laser point cloud three-dimensional scanning data acquired by the current unmanned aerial vehicle has a mass property, the sampling density is high, the point cloud distribution is dense, and the laser point cloud data which is not classified and organized is inconvenient to be put into the application of the power transmission line three-dimensional visualization.
Generally, the work of processing three-dimensional laser point cloud data of a power transmission line mainly comprises processes of pole tower position and attribute editing, line gear cutting, point cloud denoising, point cloud classification, dangerous point detection and the like, and the point cloud has the characteristics of high sampling density, dense point cloud distribution and the like, so that the classification and feature extraction of the geometric data of the point cloud are difficult to directly carry out. The point cloud classification aims to mark the acquired original laser point cloud as a ground point, a power line point, a tower point and the like, which is the basis for analyzing the safety of the power transmission line. The embodiment of the invention combines the actual requirements of a power grid, and realizes the automatic classification of the laser point cloud data through the following steps:
firstly, manually screening training samples from original laser point cloud data of a power transmission line, and considering different tower types and line types during selection in order to ensure the accuracy of classification, for example: cat head tower, wineglass tower, dry style of calligraphy tower, door type tower etc. single conductor, split conductor etc. the artifical power line and shaft tower classification of separating, utilize filter algorithm to divide out ground point, and is concrete, the basic thinking of filter algorithm is: in the point cloud data, the height difference between two points is caused by the fluctuation of the natural terrain and the height of the ground feature, and if the height difference between two adjacent points is larger, the probability that the height difference is caused by the natural terrain is smaller, and more likely, the higher point is located on the ground feature and the lower point is located on the ground.
And based on the result of the first-step manual labeling, performing feature calculation and extraction on the result of the manual labeling by using an algorithm. For the power line, a Hough linear detection algorithm is adopted, so that the interference of the discontinuity point can be reduced, and the discreteness of the data of the power line is overcome. And the tower is a gathering point group, the power line boundary is eliminated by adopting corrosion operation, and then the connected region is obtained by expansion operation. And selecting the neighborhood points of each point to calculate the characteristics.
After the feature extraction is finished, a classifier is established based on the features, the classifier is adopted to automatically classify the unclassified data, and the classified result comprises the following steps: ground points, power lines, towers, and the like. And performing speckle combination on the automatic classification result, and optimizing through a correlation criterion. It should be noted that, by adopting the technical scheme of the above embodiment of the present invention, the ground point, the power line, the vegetation point outside the tower, and the building point are also processed according to the calculation method corresponding to the tower, the data characteristics are extracted, and then the corresponding classifier is generated, which can effectively realize the classification and marking of the laser point cloud data as: ground point, vegetation point, building point, power line point and pole tower point.
Furthermore, the generated classification model is used for automatically classifying different ground objects such as towers, power lines, vegetation, the ground and the like through a deep learning algorithm, checking the classification effect of a machine, editing in a manual interaction mode, correcting the misclassified or misclassified area, and further optimizing the classification model. That is, in one embodiment, the remote sensing data classification unit is further configured to:
in the process of automatically classifying the laser point cloud data by adopting a pre-constructed classification model, the automatic classification effect is checked, the manual interaction mode is adopted for editing, and the misclassified or misclassified area is corrected to generate a new laser point cloud data sample for further optimization of the classification model.
The invention adopts a machine learning strategy to realize automatic classification of the laser point cloud data, has high processing efficiency and less manpower input, can continuously improve the classification precision along with continuous enrichment of a sample database, can well classify the laser point cloud data, and is convenient to provide standard and reliable data support for visual application of a power transmission line corridor.
Because the laser point cloud data has characteristics such as magnanimity, big data, when the system stores, usually need to disperse a plurality of nodes and store the point cloud file for the laser point cloud data volume of storage between a plurality of nodes is more balanced, and the point cloud data of unmanned aerial vehicle lidar scanning is described according to information such as transmission equipment ledger after categorised, generates the metadata of XML file form, and in practical application, XML file description content includes: the XML file is organized and managed by adopting a tree structure according to the description content, so that the system can conveniently read and write. The tree structure organizes and manages the XML file, and the system can conveniently read and write. Thus, in one embodiment, the metadata acquisition unit is further configured to:
and describing the laser point cloud data according to the power transmission equipment management ledger information, acquiring corresponding metadata, generating an XML file of the metadata, and organizing and processing the XML file by using a tree structure.
When the data is automatically put in storage, the XML file of the metadata corresponding to the laser point cloud data is automatically analyzed, the information of the line name, the voltage level, the tower section and the like of the point cloud data is obtained, the storage path of the point cloud data is determined according to the information, and finally the point cloud data and the metadata file are automatically stored to the path.
Fig. 2 shows a schematic diagram of a working principle of the metadata-based holographic digital power grid data automatic storage system in the embodiment of the present invention, as shown in fig. 2, in practical application, a user selects remote sensing data to be uploaded through an application program of a client, and then the client program can verify whether the remote sensing data and corresponding metadata meet requirements, that is, whether the remote sensing data is in a remote sensing data format and whether the corresponding metadata is complete, and the remote sensing data can be uploaded only if the remote sensing data is not complete and needs to be obtained again or manually completed. The original complete mark value of the metadata may be a corresponding MD5 value calculated based on the metadata corresponding to the local remote sensing data, and it should be noted that other quantities or calculation means capable of effectively characterizing the integrity of the metadata may also be used, which is not limited herein.
And the data initial check module 13 is configured to judge whether the format of the obtained remote sensing data is correct, calculate and determine the integrity of metadata corresponding to the remote sensing data, determine that the remote sensing data and the corresponding metadata meet the requirements when the format of the remote sensing data is correct and the corresponding metadata is complete, and complete the remote sensing data if the format of the remote sensing data is not correct and the corresponding metadata is complete.
The data initial check module 13 executes the following operations to determine whether the obtained remote sensing data format is correct:
and judging whether the format of the remote sensing data is correct or not according to the actual requirements of the application of the remote sensing data and the requirements of the design of a database, and determining the file format of the metadata information stored in the database by the remote sensing data. In one embodiment, whether the remote sensing data is in a remote sensing data format or not is judged, whether the remote sensing data is in a valid remote sensing image file format or not is usually judged, if the remote sensing data is not in the valid remote sensing image file format, adjustment processing is needed, manual adjustment can be usually adopted, and the remote sensing data is obtained again after adjustment, so that data import invalidation caused by file format errors is avoided.
The extensible markup language (XML) is different from other data expression forms, the simple structure of the XML makes the XML easy to read and write data in any application program, different programs based on the changed language can be combined with information generated under Windows, Mac OS, Linux and other platforms more easily, then the XML data can be loaded into the programs easily and analyzed, and then the results are output in an XML format. Therefore, in one embodiment, a determination is made as to whether the metadata corresponding to the remotely sensed data is in an XML file format, and the metadata is stored in the database in an XML file format.
In one embodiment, the process of determining the integrity of metadata corresponding to the remote sensing data is calculated by the data initial check module 13, and includes:
and calculating a first integrity representation value of the metadata corresponding to the obtained remote sensing data, and if the first integrity representation value is equal to the original integrity marking value of the metadata, judging that the obtained metadata is complete. In practical application, in this embodiment, an MD5 value of metadata corresponding to the obtained remote sensing data is calculated, and is compared with a locally calculated MD5 value corresponding to the metadata, if the two values are equal, it is indicated that the obtained metadata is consistent with the local metadata and is complete, and if the two values are not equal, it is indicated that the obtained metadata is incomplete and needs to be completed, and a manual completion method is generally adopted. In the embodiment, in the automatic warehousing process, nodes for format judgment, integrity judgment and manual completion processing are added, so that the correctness of data warehousing is effectively improved.
And the data uploading module 15 is configured to encrypt and upload the remote sensing data meeting the requirements and the corresponding metadata to the server based on the set data transmission protocol. The transmission protocol is set according to the data format, the data wave band, the data time phase and the resolution of the current remote sensing data. In practical applications, in order to ensure confidentiality and security of data, a specific uploading operation authority is required to upload data to a server, such as a user name and a password. And before uploading the data, carrying out encryption processing on the remote sensing data and the metadata to be transmitted according to a data transmission protocol.
The uploading and the encryption operation both need a user name and a password to be connected with a server, the data uploading is realized through a network transmission protocol, and meanwhile, the data is encrypted in the data uploading process. In an optional embodiment, because the data volume of the remote sensing data is huge and the security requirement is met, under the local area network environment, the data transmission protocol of the system adopts the FTP protocol, the FTP protocol ensures the reliable and efficient transmission of mass remote sensing data, the user authentication is realized by connecting the FTP server through the user name and the password, and the security of remote sensing data transmission is enhanced by using the FTP transmission encryption.
Furthermore, in the steps of the embodiment, the uploading of the remote sensing data and the metadata is realized by adopting the data transmission protocol corresponding to the data format, the data waveband, the data time phase and the resolution of the remote sensing data, so that the timeliness and the reliability of the uploading operation of the remote sensing data are guaranteed, and the probability of errors in the transmission process caused by the specific attributes of the remote sensing data is reduced.
And the data analysis import module 17 is configured to analyze the uploaded metadata of the remote sensing data, acquire corresponding metadata information, and automatically import the remote sensing data and the metadata into a database according to the metadata information. In practical application, after receiving the uploaded data, the server side needs to decrypt the received encrypted data.
Further, the process of automatically importing the remote sensing data and the metadata into the database by the data parsing and importing module 17 according to the metadata information includes:
and determining a storage path of the remote sensing data and the metadata according to the metadata information obtained by analysis, storing the storage path as new metadata together with the corresponding remote sensing data, the metadata and the metadata information into a database, and returning a processing result and prompt information to the user after the storage is finished. Further, in one embodiment, the data parsing import module determines a storage path of the telemetry data and the corresponding metadata by:
analyzing an XML file stored with metadata to obtain metadata information, selecting a table with fields consistent with metadata information fields in a database as a storage path of the metadata, and determining a storage position of the remote sensing data according to naming information of the remote sensing data in the metadata, wherein the fields in the metadata information are determined according to actual working condition requirements of the corresponding remote sensing data, and the naming information of the remote sensing data is set according to set rules based on the metadata information. For example, for the laser point cloud data, in this step, an XML file of metadata corresponding to the laser point cloud data is automatically parsed, information such as a line name, a voltage level, a tower section and the like of the point cloud data is acquired, and a storage path of the point cloud data is determined according to the metadata information.
In one embodiment, the metadata information corresponding to the remote sensing data is matched with the attribute information of the related table of the database, the metadata information is automatically added into the corresponding data table, and the storage position of the remote sensing data is determined according to the type and name information of the remote sensing data in the metadata, so that the remote sensing data is imported into the corresponding position. It is assumed that the remote sensing image is stored in a file form (the method also supports spatial database storage), and the organization level is a region name-time-image type. The remote sensing data automatic warehousing operation based on the metadata comprises the following steps of automatically analyzing an XML file containing the metadata through a warehousing system to obtain metadata information: the method comprises the steps that the image region name, the time, the image type, the image name and the image format are determined, the storage path of metadata corresponding to an image is determined according to fields corresponding to the information, further, the storage position of the corresponding image is determined based on the naming information of the remote sensing data corresponding to the metadata, and the metadata and the corresponding image are automatically stored in a database table corresponding to the determined path based on the storage system.
Specifically, in combination with the practical application of power transmission line inspection, when an unmanned aerial vehicle inspects the power transmission line, a physical ID tag of a power transmission device is automatically read, a system background automatically judges the dimensions of the tag, such as a tower name, an inspection task, a device type and the like corresponding to an image to form an XML file of image metadata, the image is intelligently named according to metadata information, meanwhile, the metadata information corresponding to remote sensing data is used for matching attribute information of a database correlation table, fields contained in the metadata file are consistent with fields in a database table and comprise a voltage level, a line name, a tower number, a phase, a device type, shooting time and the like, the system automatically adds the metadata information into the corresponding data table, and determines the storage position of the remote sensing data according to the naming information of the remote sensing data in the metadata, so that the remote sensing data is led into the corresponding position.
And simultaneously, automatically adding the path as a new metadata and metadata analyzed from XML into a metadata database, and automatically matching and adding each piece of metadata and corresponding attributes of the relational database through a program. The metadata database in the database is linked with the remote sensing database through the path, so that the remote sensing data and the metadata stored in the database are automatically associated, and the search and further analysis of a user are facilitated. In addition, in the embodiment, the system can automatically set the naming information corresponding to the remote sensing data according to the set rule based on the metadata information, so that the workload of image arrangement and classification by workers is greatly reduced, and the intellectualization and automation level of the system is greatly improved.
By adopting the technical scheme, automatic warehousing of the remote sensing data and the corresponding metadata can be realized, the warehousing process is simplified, the defects of time and labor waste, poor confidentiality and high error probability in the traditional automatic storage technology in the prior art are overcome, meanwhile, the processing module is arranged in the automatic warehousing process to supervise the format and integrity of the data, the error rate of the data is controlled to be kept at an extremely low degree, the accuracy of the data is improved on the premise of ensuring the intelligentization of data importing, and the reasonable utilization of the space of the database is facilitated to a certain extent;
in addition, the embodiment of the invention provides a technical means for realizing automatic classification of laser point cloud data based on a machine learning strategy, so that the laser point cloud data automatically put in storage has matched classification marks, which is favorable for providing a reliable and standard data base for three-dimensional visualization application of a power transmission line corridor.
Example two
Integrity check is carried out on the remote sensing data uploaded to the server and the corresponding metadata, the integrity check is one of effective means for guaranteeing the success rate of data transmission, after the check is passed, the data are analyzed and then are led into the database, redundant operation caused by data transmission failure can be reduced to a great extent, and the storage pressure of the database is relieved to a certain extent. Based on this, fig. 3 shows a schematic structural diagram of the metadata-based holographic digital grid data automatic warehousing system in the embodiment of the present invention, and as can be seen from fig. 3, the system includes: the system comprises a data acquisition module, a data initial check module, a data uploading module, a data rechecking module and a data analysis and import module;
in this embodiment, the system may further include: and the data rechecking module 31 is arranged between the data uploading module and the data analysis import module, is configured to check the integrity of metadata corresponding to the uploaded remote sensing data, and if the metadata is incomplete, feeds back a check result to the data uploading module and uploads the metadata again by the data uploading module.
Specifically, the data rechecking module 31 performs the following operations to check the integrity of the metadata corresponding to the uploaded remote sensing data:
and calculating a second integrity characteristic value of the metadata corresponding to the uploaded remote sensing data, comparing the second integrity characteristic value with an original integrity marking value or a first integrity characteristic value corresponding to the metadata, and if the second integrity characteristic value is equal to the original integrity marking value or the first integrity characteristic value, indicating that the transmitted metadata is complete.
In this embodiment, after data is uploaded, an integrity characterizing value corresponding to the data is calculated, where the integrity characterizing value is an MD5 calculated value of the metadata, and the MD5 calculated value is compared with a local MD5 calculated value corresponding to the metadata or an MD5 value calculated after data acquisition, and if the integrity characterizing value is equal to any one, it indicates that the integrity of the data is not affected in the data transmission process, so as to effectively ensure the integrity of the remote sensing data and the metadata in the data transmission process, wherein an MD5 algorithm is a hash function widely used in the computer security field, input information is processed in 512-bit groups, each group is divided into 16 32-bit sub-groups, after a series of processing, the output of the algorithm is composed of 4 32-bit groups, and a 128-bit hash value is generated after the 4 32-bit groups are concatenated. By comparing MD5 values before and after uploading of remote sensing data, integrity protection of the data is provided, and accuracy of a data analysis result and correctness of the data after storage are further guaranteed.
In the metadata-based holographic digital power grid data automatic storage system provided by the embodiment of the invention, each module or unit structure can be independently operated or operated in a combined mode according to actual requirements so as to realize corresponding technical effects.
EXAMPLE III
Based on other aspects of any one or more of the foregoing embodiments, an embodiment of the present invention further provides a metadata-based remote sensing data automatic warehousing method, fig. 4 shows a flow diagram of the metadata-based remote sensing data automatic warehousing method in this embodiment, and as can be seen with reference to information in fig. 4, the method includes the following steps:
and S410, acquiring professional remote sensing data of power transmission, power transformation and power distribution with warehousing requirements in a power grid through a data acquisition module, and determining metadata corresponding to the remote sensing data and an original complete mark value of the metadata, wherein the remote sensing data comprises visible light photographic data and laser point cloud data.
And step S420, judging whether the format of the obtained remote sensing data is correct by using a data initial check module, calculating and determining the integrity of metadata corresponding to the remote sensing data, determining that the remote sensing data and the corresponding metadata meet the requirements when the format of the remote sensing data is correct and the corresponding metadata is complete, and completing the remote sensing data if the format of the remote sensing data is not correct and the corresponding metadata is complete.
And S430, encrypting and uploading the remote sensing data meeting the requirements and the corresponding metadata to a server by using a set data transmission protocol through a data uploading module, wherein the transmission protocol is set according to the data format, the data waveband, the data time phase and the resolution of the current remote sensing data.
And S450, analyzing the uploaded metadata of the remote sensing data by using a data analysis import module, acquiring corresponding metadata information, and automatically importing the remote sensing data and the metadata into a database according to the metadata information.
In the steps of the embodiment, the uploading of the remote sensing data and the metadata is realized by adopting the data transmission protocol corresponding to the data format, the data wave band, the data time phase and the resolution of the remote sensing data, so that the timeliness and the reliability of the uploading operation of the remote sensing data are guaranteed, and the error probability in the transmission process caused by the special attribute of the remote sensing data is reduced.
Further, in one embodiment, the step S410 includes the following operations:
calling corresponding remote sensing data from a remote sensing data acquisition equipment terminal according to the warehousing requirements of users;
dividing visible light photographic data and laser point cloud data according to a data format of the accessed remote sensing data, and automatically classifying the laser point cloud data by adopting a pre-constructed classification model; in the step, classification labels corresponding to the laser point cloud data can be obtained through automatic classification, and the obtained classification labels and the corresponding laser point cloud data are stored in an associated mode to serve as the laser point cloud data after automatic classification.
Describing the visible light photographic data and the automatically classified laser point cloud data, acquiring corresponding metadata, and generating an original complete marking value representing the integrity of the metadata;
the classification model is obtained by training based on a screened training sample and a deep learning strategy.
Specifically, the classification model is trained by the following steps:
screening a set number of manual training samples from the existing laser point cloud data samples by workers considering different line types and different tower types;
determining ground points in a training sample by using a filtering algorithm, and further manually dividing sample data except the ground points in the training sample into tower data, power line data and other types of data, wherein the other types of data represent other point cloud data except the ground points, the tower data and the power line data;
selecting neighborhood points of each point, respectively calculating data characteristics of power line data and tower data, and constructing a classifier according to the data characteristics;
and automatically classifying other samples except the manual training sample in the laser point cloud data sample in batches by using the classifier, performing speckle combination on the automatic classification result to realize optimization of the classifier until the classification precision of the classifier meets a set condition, and taking the obtained classifier as a classification model.
The data characteristics of the power line data and the tower data are calculated through the following operations:
for power line data, calculating data characteristics of cloud data of each point by adopting a Hough linear detection algorithm; for tower data, firstly, corrosion operation is adopted to eliminate the power line boundary, and then expansion operation is utilized to obtain the communication area of the tower gathering point group.
In one embodiment, the step S410 further includes: in the process of automatically classifying the laser point cloud data by adopting a pre-constructed classification model, the automatic classification effect is checked, the manual interaction mode is adopted for editing, and the misclassified or misclassified area is corrected to generate a new laser point cloud data sample for further optimizing the classification model
Further, the step S410 may further include: and describing the laser point cloud data according to the power transmission equipment management ledger information, acquiring corresponding metadata, generating an XML file of the metadata, and organizing and processing the XML file by using a tree structure.
In one embodiment, before step S450, the method further includes:
s440, checking the integrity of metadata corresponding to the uploaded remote sensing data, if the metadata are incomplete, feeding back a checking result to the data uploading module, and uploading the result again by the data uploading module;
in step S440, checking the integrity of the metadata corresponding to the uploaded remote sensing data, including calculating a second integrity characterizing value of the metadata corresponding to the uploaded remote sensing data, comparing the second integrity characterizing value with an original integrity marking value or a first integrity characterizing value corresponding to the metadata, and if the second integrity characterizing value is equal to any one of the original integrity marking value or the first integrity characterizing value, indicating that the transmitted metadata is complete.
In one embodiment, in step S420, the process of determining whether the obtained remote sensing data format is correct by the data initial checking module includes:
and judging whether the format of the remote sensing data and the format of the corresponding metadata are correct or not according to the actual requirements of the application of the remote sensing data and the requirements of the design of the database, and determining the file format for storing the metadata information in the database.
In one embodiment, in step S430, the process of computationally determining the integrity of metadata corresponding to telemetry data includes:
and calculating a first integrity representation value of the metadata corresponding to the obtained remote sensing data, and if the first integrity representation value is equal to the original integrity marking value of the metadata, judging that the obtained metadata is complete.
In one embodiment, in step S450, the process of automatically importing the remote sensing data and the metadata into the database according to the metadata information includes:
and determining a storage path of the remote sensing data and the metadata according to the metadata information obtained by analysis, combining the storage path and the metadata to be used as new metadata, storing the new metadata and the corresponding remote sensing data and the metadata information into a database, and returning a processing result and prompt information to a user after the storage is finished.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, appearances of the phrase "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (12)
1. A holographic digital power grid data automatic storage system based on metadata is characterized by comprising:
the system comprises a data acquisition module, a storage module and a storage module, wherein the data acquisition module is configured to acquire professional remote sensing data of power transmission, power transformation and power distribution with storage requirements in a power grid, and determine metadata corresponding to the remote sensing data and an original complete mark value of the metadata, wherein the remote sensing data comprise visible light photographic data and laser point cloud data;
the data initial check module is configured to judge whether the format of the obtained remote sensing data is correct or not, calculate and determine the integrity of metadata corresponding to the remote sensing data, and determine that the remote sensing data and the corresponding metadata meet requirements when the format of the remote sensing data is correct and the corresponding metadata are complete; the data uploading module is configured to encrypt and upload the remote sensing data meeting the requirements and the corresponding metadata to a server based on a set transmission protocol; the transmission protocol is set according to the data format, the data wave band, the data time phase and the resolution of the current remote sensing data;
and the data analysis import module is configured to analyze the uploaded metadata of the remote sensing data, acquire corresponding metadata information, and automatically import the remote sensing data and the metadata into a database according to the metadata information.
2. The system of claim 1, wherein the data acquisition module comprises:
the remote sensing data acquisition unit is configured to call corresponding remote sensing data from a remote sensing data acquisition equipment terminal or receive remote sensing data uploaded by a user according to the warehousing requirement of the user;
the remote sensing data classification unit is configured to divide visible light shooting data and laser point cloud data according to a data format of the remote sensing data which is obtained, and automatically classify the laser point cloud data by adopting a pre-constructed classification model;
the metadata acquisition unit is configured to describe the visible light photographic data and the automatically classified laser point cloud data, acquire corresponding metadata and generate an original complete mark value representing the integrity of the metadata;
the classification model is obtained by training based on a screened training sample and a deep learning strategy.
3. The system of claim 1 or 2, wherein the data acquisition module trains the classification model by:
screening a set number of manual training samples from the existing laser point cloud data samples by workers considering different line types and different tower types;
determining ground points in a training sample by using a filtering algorithm, and further manually dividing sample data except the ground points in the training sample into tower data, power line data and other types of data, wherein the other types of data represent other point cloud data except the ground points, the tower data and the power line data;
selecting neighborhood points of each point, respectively calculating data characteristics of power line data and tower data, and constructing a classifier according to the data characteristics;
and automatically classifying other samples except the manual training sample in the laser point cloud data sample in batches by using the classifier, performing speckle combination on the automatic classification result to realize optimization of the classifier until the classification precision of the classifier meets a set condition, and taking the obtained classifier as a classification model.
4. The system of claim 2, wherein the remote sensing data classification unit is further configured to:
in the process of automatically classifying the laser point cloud data by adopting a pre-constructed classification model, the automatic classification effect is checked, the manual interaction mode is adopted for editing, and the misclassified or misclassified area is corrected to generate a new laser point cloud data sample for further optimization of the classification model.
5. The system of claim 2, wherein the metadata acquisition unit is further configured to:
and describing the laser point cloud data according to the power transmission equipment management ledger information, acquiring corresponding metadata, generating an XML file of the metadata, and organizing and processing the XML file by using a tree structure.
6. The system of claim 3, wherein the data acquisition module calculates the data characteristics of the power line data and the tower data, respectively, by:
for power line data, calculating data characteristics of cloud data of each point by adopting a Hough linear detection algorithm; for tower data, firstly, corrosion operation is adopted to eliminate the power line boundary, and then expansion operation is utilized to obtain the communication area of the tower gathering point group.
7. The system of claim 1, wherein the data primary check module is further configured to:
and judging whether the format of the remote sensing data is correct or not according to the actual requirements of the application of the remote sensing data and the requirements of the design of a database, and determining the file format of the metadata information stored in the database by the remote sensing data.
8. The system of claim 1 or 7, wherein the data primary check module is further configured to:
and calculating a first integrity representation value of the metadata corresponding to the obtained remote sensing data, and if the first integrity representation value is equal to the original integrity marking value of the metadata, judging that the obtained metadata is complete.
9. The system of claim 1, wherein the system further comprises:
the data rechecking module is configured to check the integrity of metadata corresponding to the uploaded remote sensing data, if the metadata are incomplete, the check result is fed back to the data uploading module, and the metadata are uploaded again by the data uploading module;
the data rechecking module checks the integrity of metadata corresponding to the uploaded remote sensing data through the following steps:
and calculating a second integrity characteristic value of the metadata corresponding to the uploaded remote sensing data, comparing the second integrity characteristic value with an original integrity marking value or a first integrity characteristic value corresponding to the metadata, and if the second integrity characteristic value is equal to any one of the first integrity characteristic values, indicating that the transmitted metadata is complete.
10. The system of claim 1, wherein the data parsing import module automatically imports the telemetry data and the metadata into the database by performing the operations of:
and determining a storage path of the remote sensing data and the metadata according to the metadata information obtained by analysis, combining the storage path and the metadata to be used as new metadata, storing the new metadata and the corresponding remote sensing data and the metadata information into a database, and returning a processing result and prompt information to a user after the storage is finished.
11. The system of claim 10, wherein the data parsing import module determines a storage path for the telemetry data and corresponding metadata by:
analyzing a file stored with metadata to obtain metadata information, selecting a table with fields in a database consistent with the obtained metadata information fields as a storage path of the current metadata, and further determining the storage position of the remote sensing data according to the naming information of the remote sensing data in the metadata, wherein the fields in the metadata information are determined according to the actual working condition requirements of the corresponding remote sensing data, and the naming information of the remote sensing data is set according to a set rule based on the metadata information.
12. A method for automatically warehousing remote sensing data based on metadata, which is applied to the system of any one of claims 1 to 11.
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