Data management method and device for distribution network hot-line work robot

Abstract

The invention discloses a data management method and device for a distribution network hot-line work robot, and the method comprises the steps: collecting work data based on a linux system, and checking a roSTpic list corresponding to the work data; recording and compressing the job data through rosbag to generate a compressed file; encrypting the compressed file through an openssl instruction to generate an encrypted file; the encrypted file is uploaded to a cloud server, the encrypted file is decrypted through the openssl instruction, and a second bag file is generated; and performing data processing on the second bag file. According to the method and device provided by the invention, manual operation can be greatly reduced, and operation data processing is facilitated; the storage space is saved, and more operation data can be stored.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a data management method and device for a live-line operation robot for a distribution network. Background Art

[0002] Live distribution network operations have become the most direct and effective means for power supply companies to reduce power outage time, improve power supply reliability and service levels. The main operations are 10kV live disconnection, bolt tightening, tree pruning, grounding ring installation, lightning arrester replacement, etc. However, manual operations are labor-intensive and have high safety risks. Therefore, live distribution network operation robots are widely used at home and abroad.

[0003] At present, the operation data of live-working robots mainly rely on manual records, which is prone to the following problems: 1. Live-working robots have many sensors, and each sensor has a different output interface, which easily leads to more transmission protocols, such as network transmission, socket, CAN, etc., resulting in the collected operation data being more complex, difficult to process, and inefficient; 2. The amount of operation data is large, requiring a huge storage space. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a data management method and device for a distribution network live operation robot, which can realize standardized management of operation data and store more operation data.

[0005] In a first aspect, an embodiment of the present invention provides a data management method for a live-line operation robot of a distribution network, characterized by comprising:

[0006] Collect job data based on the Linux system and view the rostopic list corresponding to the job data.

[0007] Record and compress the job data through rosbag to generate a compressed file;

[0008] Encrypt the compressed file through openssl command to generate an encrypted file;

[0009] Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate a second bag file;

[0010] Perform data processing on the second bag file.

[0011] Preferably, the job data is recorded and a first bag file is generated through a rosbag record instruction; the first bag file is compressed and a compressed file is generated through a rosbag compress instruction.

[0012] Preferably, a public key file and a private key file are generated by the openssl instruction, the public key file is used to encrypt the compressed file, and the private key file is used to decrypt the encrypted file.

[0013] Preferably, the encrypted file is uploaded to the cloud server via FTP protocol.

[0014] Preferably, data processing is performed on the second bag file, including:

[0015] Extracting data from the second bag file to obtain robot operation process data and visual data;

[0016] Classifying the robot operation process data according to the data type, and saving the classified robot operation process data;

[0017] The visual data is labeled according to target recognition objects to establish a data set.

[0018] Preferably, the robot operation process data is saved as a file in json format.

[0019] In a second aspect, an embodiment of the present invention provides a data management device for a live-line operation robot of a distribution network, characterized in that it includes:

[0020] Data collection module: used to collect job data based on the Linux system and view the rostopic list corresponding to the job data;

[0021] Data compression module: records and compresses the job data through rosbag to generate a compressed file;

[0022] Data encryption and decryption module: encrypt the compressed file through openssl instructions to generate an encrypted file;

[0023] Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate a second bag file;

[0024] Data management module: performs data processing on the second bag file.

[0025] The beneficial effects of the present invention are as follows:

[0026] It reduces manual operations, facilitates the processing of operation data, and has a higher degree of autonomy; it saves storage space and can store more operation data; it encrypts operation data, effectively improving data security.

[0027] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 A schematic flow chart of a data management method for a live-line operation robot for a distribution network provided in an embodiment of the present invention;

[0031] Figure 2 A schematic structural diagram of a data management device for a live-working robot in a distribution network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] To facilitate understanding of this embodiment, Figure 1 A data management method and device for a live-working robot for a distribution network disclosed in an embodiment of the present invention are introduced in detail.

[0034] Embodiment 1:

[0035] A data management method for a live-line operation robot for a distribution network, comprising:

[0036] S1: Collect job data based on the Linux system and view the rostopic list corresponding to the job data.

[0037] Among them, the operation data includes: operation environment information data and live operation robot information data. The operation environment information data is obtained by the survey equipment, and the live operation robot information data is obtained by the main control system.

[0038] S2: Record and compress the job data through rosbag to generate a compressed file.

[0039] Preferably, the job data is recorded through the rosbag record instruction and a first bag file is generated; the first bag file is compressed through the rosbagcompress instruction and a compressed file is generated.

[0040] The file suffix of the first bag file is .bag; the compression format of the compressed file is LZ4.

[0041] S3: Encrypt the compressed file through the openssl command to generate an encrypted file;

[0042] Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate the second bag file.

[0043] Preferably, a public key file and a private key file are generated by openssl instructions, the public key file is used to encrypt the compressed file, and the private key file is used to decrypt the encrypted file.

[0044] Preferably, the encrypted file is uploaded to the cloud server via FTP protocol.

[0045] Among them, the FTP protocol is the File Transfer Protocol (File Transfer Protocol).

[0046] S4: Perform data processing on the second bag file.

[0047] Preferably, data processing is performed on the second bag file, including:

[0048] S401: extracting data from the second bag file to obtain robot operation process data and visual data;

[0049] S402: Classify the robot operation process data according to the data type, and save the classified robot operation process data; annotate the visual data according to the target recognition object and establish a data set.

[0050] Visual data includes: point cloud data, image data; the data set can be applied to the training and learning of live working tasks, and the data set includes a training set and a test set; target recognition objects include but are not limited to: utility poles, main line branches, insulators, lightning arresters, bird repellers, and grounding rings.

[0051] In this embodiment, the visual data is annotated by combining manual annotation and semi-automatic annotation to improve annotation efficiency and accuracy.

[0052] Among them, the manual labeling method includes that the operator uses professional labeling tools, such as LabelImg tools or CVAT tools, to label the collected visual data in detail, including selecting the area of ​​interest and labeling the category. The area includes tools, obstacles and target objects detected by the robot during operation. This method is mainly used for data labeling in complex scenarios to ensure the high accuracy of key data.

[0053] Semi-automatic labeling methods include: For large-scale visual data, the system first uses existing target detection models, such as the YOLO model or the Mask R-CNN model, to automatically perform preliminary labeling. The system will generate a bounding box or polygonal outline of the object and automatically predict the object category and attributes. These automatically generated labeling results will be manually proofread and adjusted to ensure the accuracy of the labeling results and avoid incorrect labeling due to model misjudgment. In the semi-automatic labeling process, the depth information collected by the robot is also integrated with the visual data to assist in labeling targets with blurred boundaries, further improving the labeling accuracy.

[0054] Furthermore, the functions of the operation process data include:

[0055] 1. Optimize robot performance evaluation: Operation process data can help evaluate the robot's performance in executing tasks, analyze which links are inefficient or prone to errors, and adjust operation strategies and algorithms in a timely manner for optimization. For example, analyzing the average speed, deviation, and execution time of the robot in a handling task can help optimize path planning and motion control algorithms.

[0056] 2. Prevent and diagnose faults: By monitoring the robot's operating data in real time, potential fault risks can be discovered. For example, if the current consumption suddenly increases, it may indicate a problem with the mechanical system. Obtaining this data in a timely manner and analyzing it can effectively avoid large-scale robot failures.

[0057] 3. Optimize the execution of tasks: Based on real-time task progress data, the robot can dynamically adjust the task execution order or strategy according to environmental changes. For example, if there are sudden obstacles in the working environment, the robot can adjust the path based on the obstacle detection data to avoid task interruption.

[0058] 4. Improve the safety of robot operations: Through real-time feedback of abnormal detection data and sensor data, the system can identify potential sources of danger in advance (such as operating errors or changes in the external environment) and intervene in time to ensure the safety of robot operations. For example, when the robot encounters high temperature or abnormal pressure, the system will automatically trigger an alarm.

[0059] 5. Evaluate and predict task efficiency: By analyzing various data during the operation, we can evaluate the efficiency of task execution and predict the performance of future tasks. For example, we can estimate the completion time of similar tasks in the future based on historical task data and make preparations in advance.

[0060] 6. Facilitate modeling of the working environment: By collecting environmental data, such as temperature, humidity, object position, etc., an accurate working environment model can be built for the robot. This helps to achieve higher-precision task execution in complex environments, such as working in a small space or unstable working environment.

[0061] Furthermore, the robot operation process data is saved as a file in json format.

[0062] The beneficial effects of the data management method provided in this embodiment include:

[0063] 1. Through the standardized management of operation data, tedious manual operations are avoided and the autonomy is high;

[0064] 2. By compressing to store more job data and encrypting the job data, data security is effectively improved.

[0065] 3. Define different file formats for different job data, and bring the following beneficial effects:

[0066] (1) Improve the standardization and readability of data management:

[0067] Different types of operation data use specially defined file formats (such as point cloud data uses .pcd format, image data uses .jpg or .png format, and operation process records use .json format) to clearly indicate the type and purpose of the data. Therefore, the standardized management of operation data makes it easier for subsequent developers to quickly identify data types and directly use corresponding tools or libraries for parsing and processing, reducing the risk of data misunderstanding or file confusion.

[0068] (2) Facilitate subsequent data processing and analysis:

[0069] In practical applications, designing appropriate file formats based on the different characteristics of job data can speed up data processing and reduce additional format conversion overhead. For example, storing point cloud data in .pcd format can retain three-dimensional information; image data uses a universal image format for compatibility with deep learning frameworks; and job log data is saved in .json format for structured storage and retrieval.

[0070] (3) Improve data storage and transmission efficiency:

[0071] By properly selecting the file format, storage space and transmission time can be significantly reduced. For example, point cloud data can reduce redundant information through specific formats, such as compressed .pcd, image data can reduce file size through lossy or lossless compression, and the .json format is suitable for storing structured information of key-value pairs, which is convenient for small and efficient transmission.

[0072] (4) Enhance cross-platform and cross-tool compatibility:

[0073] Defining different file formats can be aligned with industry standards, ensuring that data can be directly used by a variety of tools and platforms. For example, point cloud data in .pcd format can be directly loaded and processed in PCL (Point Cloud Library); job logs in .json format can be parsed by mainstream programming languages; and the standard format of image data can be directly used for training and testing visual models. This can reduce the need for the development of customized tools and improve the scalability and compatibility of the system.

[0074] (5) Supporting flexibility in data sharing and distribution:

[0075] After classifying the data by file format, you can share only certain types of files as needed (for example, share point cloud data with point cloud algorithm developers, share visual images with target recognition model developers), without providing all the original data. This flexibility helps protect data privacy while facilitating efficient collaboration between different teams.

[0076] (6) Convenient for version control and data verification:

[0077] Defining different file formats makes it easier to clearly identify the changes when updating data. For example, when updating point cloud data, you only need to replace the .pcd file, which will not affect the job log in the .json file; each type of file can be verified independently, for example, the integrity of the file can be verified by the MD5 checksum, avoiding version management problems when multiple types of data are mixed together.

[0078] (7) Support multi-task parallel processing:

[0079] Different tasks may have different requirements for operating data. For example, visual algorithms only require image data, while path planning may focus more on point cloud data. By defining different file formats, each module can process related data in parallel, significantly improving the overall system's operating efficiency and task completion speed.

[0080] Embodiment 2:

[0081] A data management device for a live-line operation robot for a distribution network, comprising:

[0082] Data collection module: used to collect job data based on the Linux system and view the rostopic list corresponding to the job data;

[0083] Data compression module: records and compresses job data through rosbag to generate compressed files;

[0084] Data encryption and decryption module: encrypt compressed files through openssl instructions to generate encrypted files;

[0085] Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate the second bag file;

[0086] Data management module: performs data processing on the second bag file.

[0087] Among them, a lightweight deployment device of a distribution network model based on knowledge distillation disclosed in this embodiment can realize all the contents of a data management method for a distribution network live working robot disclosed in Example 1 of this application.

[0088] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A data management method for a live-line working robot of a distribution network, characterized in that: include: Collect job data based on the Linux system and view the rostopic list corresponding to the job data; Record and compress the job data through rosbag to generate a compressed file; Encrypt the compressed file through openssl command to generate an encrypted file; Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate a second bag file; Perform data processing on the second bag file.

2. The data management method of a live-line working robot for a distribution network according to claim 1, characterized in that: The job data is recorded through the rosbag record instruction and a first bag file is generated; the first bag file is compressed through the rosbag compress instruction and a compressed file is generated.

3. The data management method of a live-line working robot for a distribution network according to claim 1, characterized in that: A public key file and a private key file are generated by the openssl instruction, the public key file is used to encrypt the compressed file, and the private key file is used to decrypt the encrypted file.

4. The data management method for a live-line working robot for a distribution network according to claim 1, characterized in that: The encrypted file is uploaded to the cloud server via the FTP protocol.

5. The data management method of a live-line working robot for a distribution network according to claim 1, characterized in that: Perform data processing on the second bag file, including: Extracting data from the second bag file to obtain robot operation process data and visual data; Classifying the robot operation process data according to the data type, and saving the classified robot operation process data; The visual data is labeled according to the target recognition object to establish a data set.

6. The data management method of a live-line working robot for a distribution network according to claim 5, characterized in that: The robot operation process data is saved as a file in json format.

7. A data management device for a live-line operation robot of a distribution network, characterized in that: include: Data collection module: used to collect job data based on the Linux system and view the rostopic list corresponding to the job data; Data compression module: records and compresses the job data through rosbag to generate a compressed file; Data encryption and decryption module: encrypt the compressed file through openssl instructions to generate an encrypted file; Upload the encrypted file to the cloud server, decrypt the encrypted file through the openssl command, and generate a second bag file; Data management module: performs data processing on the second bag file.

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