Data management system and method for power transmission line

Abstract

The invention relates to a data management system and method for a power transmission line. Comprising a data acquisition terminal, a data processing module and an operation support module, the data acquisition terminal is used for monitoring a power transmission line to obtain management data corresponding to the power transmission line; the data processing module is used for performing pre-calculation processing on the management data through a streaming calculation mode and an offline calculation mode to obtain processed management data; the operation support module is used for generating a key index and a defect identification result based on the processed management data; and the operation support module is also used for determining a risk level based on the defect identification result and the key index, and displaying the risk level and the defect identification result in a preset terminal display module. According to the embodiment of the invention, the whole flow process from planning, data acquisition, analysis, decision making and display is realized, and data of different subsystems can be effectively intersected and analyzed.

Description

Technical Field

[0001] This application relates to the field of power transmission line technology, and in particular to a data management system and method for power transmission lines. Background Technology

[0002] As the scale of power transmission lines continues to expand, the quantity and types of power transmission-related data are experiencing explosive growth. Numerous monitoring devices and analysis systems deployed along power transmission lines generate massive amounts of different types of operation and maintenance management data, which comprehensively reflect the operating environment and status of the power transmission lines.

[0003] In related technologies, the operation and maintenance management of transmission lines mainly relies on various professional subsystems (such as data monitoring, inspection management, fault diagnosis, etc.) to achieve data monitoring and transmission line management in their respective aspects. These heterogeneous subsystems independently collect, store, and analyze data. They are separated in hardware, do not communicate with each other in software and data, and are incompatible in communication protocols, forming data silos. This leads to problems in communication and resource sharing among different parts of the operation and maintenance management of transmission lines, and data from different subsystems cannot be effectively exchanged and analyzed. Summary of the Invention

[0004] Therefore, it is necessary to provide a data management system and method for power transmission lines to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a data management system for transmission lines, comprising:

[0006] Data acquisition terminals are used to monitor transmission lines and obtain management data corresponding to the transmission lines;

[0007] The data processing module is used to pre-process management data through streaming computing mode and offline computing mode to obtain processed management data;

[0008] The operation support module is used to generate key indicators and defect identification results based on the processed management data;

[0009] The operation support module is also used to determine the risk level based on the defect identification results and key indicators, and to display the risk level and defect identification results in the preset terminal display module.

[0010] In one embodiment, the system further includes an Internet of Things (IoT) module, which comprises a data acquisition submodule and a communication submodule.

[0011] The data acquisition submodule is used to acquire management data collected by the data acquisition terminal;

[0012] The communication submodule is used to send management data to the data processing module.

[0013] In one embodiment, the system further includes a cloud platform module;

[0014] The cloud platform module provides cloud resources to the data management system. These resources include computing resources, storage resources, network resources, and microservice management resources. The computing resources support the streaming and offline computing of the data processing modules. The storage resources provide data storage capabilities for the data management system. The network resources provide data transmission capabilities for the data management system. The microservice management resources support the operation support modules in calculating key indicators, identifying and handling defects, and determining and displaying risk levels.

[0015] In one embodiment, the data acquisition terminal includes a visualization-type monitoring terminal, a status monitoring terminal, a drone monitoring terminal, a robot monitoring terminal, and a behavior monitoring terminal.

[0016] In one embodiment, the operation support module includes a key indicators submodule;

[0017] The key metrics submodule is used to generate key metrics based on the processed management data. Key metrics include anomaly indicators, work order execution indicators, plan execution indicators, terminal operation indicators, and defect and potential hazard indicators.

[0018] In one embodiment, the data processing module includes a data bridging submodule and a data fetching submodule;

[0019] The data bridging submodule is used to convert the management data collected by each acquisition terminal into a preset standard data format;

[0020] The data scraping submodule is used to scrape preset expected data from outside the data management system.

[0021] In one embodiment, the operation support module further includes an auxiliary decision-making submodule and a risk management submodule;

[0022] The decision support submodule is used to call the preset identification model and calculate the defect identification result based on the processed management data.

[0023] The risk management submodule is used to quantify the corresponding risk level by calling a preset risk assessment model based on the defect identification results.

[0024] In one embodiment, the operation support module further includes a system management submodule;

[0025] The system management submodule is used to set the basic operating configuration of the data management system.

[0026] In one embodiment, the terminal display module includes a mobile display terminal and a fixed display terminal.

[0027] Secondly, this application also provides a data management method for transmission lines, applied to the data management system described above, including:

[0028] Obtain management data corresponding to transmission lines;

[0029] The management data is processed through streaming computing and offline computing modes to obtain the processed management data.

[0030] Key indicators are generated based on the processed management data, and defect identification results are calculated based on the key indicators and the processed management data.

[0031] The risk level is determined based on the defect identification results, and the risk level and defect identification results are displayed in the preset terminal display module.

[0032] The aforementioned data management system and method for power transmission lines includes a data acquisition terminal, a data processing module, and an operation support module. The data acquisition terminal monitors the power transmission lines to obtain management data corresponding to them. The data processing module pre-processes the management data using streaming and offline computing modes to obtain processed management data. The operation support module generates key indicators and defect identification results based on the processed management data. Furthermore, the operation support module determines risk levels based on the defect identification results and key indicators, and displays the risk levels and defect identification results in a pre-defined terminal display module. This application's embodiments realize a complete process from planning, data acquisition, analysis, decision-making, and display, significantly reducing the workload of manual inspections and data processing. Furthermore, this application can aggregate data acquisition results from multiple terminals, thereby obtaining more comprehensive and accurate analysis and identification results. This application also converts discrete defects into quantifiable risk levels, providing more intuitive calculation results and facilitating the optimization of operation and maintenance strategies. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of the structure of a data management system for a power transmission line in one embodiment;

[0035] Figure 2 This is a schematic diagram of the structure of a data management system for a transmission line in a preferred embodiment;

[0036] Figure 3 This is a flowchart illustrating a data management method for transmission lines in another embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] The following is a brief explanation of the technical background of this application:

[0039] With the continuous expansion of my country's power transmission line scale and the rapid development of technologies such as communication, the Internet, and the Internet of Things, the operational data of power transmission lines has experienced explosive growth in both quantity and variety, marking the full entry of the power transmission sector into the era of big data. Numerous monitoring devices deployed on the lines, various inspection operations, and specialized analysis systems have collectively generated massive and diverse operation and maintenance management data that comprehensively reflects the operating environment and status of the lines. This data has become a valuable resource driving the industry's development.

[0040] However, this valuable operation and maintenance data currently faces severe challenges: the data sources are diverse and heterogeneous in format, lacking unified management standards; the data is scattered across different systems, resulting in duplicate storage or inconsistencies. This situation directly leads to significant deficiencies in the operation and maintenance management of transmission lines: communication and resource sharing between departments are difficult, and the business functions of each system are limited, with high barriers to data sharing. As a result, massive amounts of operation and maintenance data cannot be effectively aggregated and collaboratively analyzed; data analysis functions are limited and simplistic, making it difficult to support comprehensive decision-making applications, let alone form an intelligent operation and maintenance closed loop for transmission operations from monitoring to analysis to decision-making. This prevents the release of the intelligent analysis potential inherent in massive amounts of data, greatly limiting the supporting role of transmission big data in intelligent and digital development, and making it difficult to transform related research results into intelligent auxiliary decision-making tools usable by operation and maintenance management personnel.

[0041] In summary, existing data management challenges and system barriers have become key bottlenecks restricting the development of power transmission lines towards advanced stages of intelligent and digital power transmission. Therefore, building a new management system capable of breaking down data silos and enabling intelligent analysis and collaborative decision-making is of urgent need and significance.

[0042] In summary, the above description illustrates the main problems existing in the relevant technologies: In these technologies, the operation and maintenance management of transmission lines mainly relies on various professional subsystems to achieve data monitoring and transmission line management in their respective stages. These heterogeneous subsystems independently collect, store, and analyze data. They are separated in terms of hardware, software, and data, and their communication protocols are incompatible, forming data silos. This results in problems with smooth communication and resource sharing among different parts of the operation and maintenance management of transmission lines. Data from different subsystems cannot be effectively integrated and analyzed, relying on fragmented integration by manual labor, and failing to achieve a complete intelligent operation and maintenance closed-loop structure.

[0043] Based on this, this application provides a data management system for transmission lines: the system includes a data acquisition terminal, a data processing module, and an operation support module, comprising: an acquisition terminal for monitoring transmission lines and obtaining management data corresponding to the transmission lines; a data processing module for pre-processing the management data through streaming computing and offline computing modes to obtain processed management data; an operation support module for generating key indicators and defect identification results based on the processed management data; and an operation support module for determining risk levels based on defect identification results and key indicators, and displaying the risk levels and defect identification results in a preset terminal display module. This application realizes the sharing of transmission management-related data, breaks down the barriers between different platforms / subsystems such as monitoring, business, and data analysis, achieves efficient data utilization, and saves the human resource costs increased due to information silos. See the following embodiments for details:

[0044] In one exemplary embodiment, such as Figure 1 As shown, a data management system for power transmission lines is provided. The system includes a data acquisition terminal 10, a data processing module 20, and an operation support module 30.

[0045] The data acquisition terminal 10 is used to monitor the transmission lines and obtain management data corresponding to the transmission lines;

[0046] Data processing module 20 is used to pre-process management data through streaming computing mode and offline computing mode to obtain processed management data;

[0047] The operation support module 30 is used to generate key indicators and defect identification results based on the processed management data;

[0048] The operation support module 30 is also used to determine the risk level based on the defect identification results and key indicators, and to display the risk level and defect identification results in the preset terminal display module.

[0049] The data acquisition terminal 10 is a collection of intelligent devices deployed at the transmission line site to acquire various status information of the line itself and its surrounding environment. Management data refers to the raw, unprocessed monitoring data generated by the acquisition terminal during the monitoring process, such as images, video streams, and data packets. The data processing module 20 is mainly responsible for processing and integrating the massive heterogeneous data collected by the acquisition terminal. The streaming computing mode and offline computing mode are two different main computing modes. Specifically, streaming computing can perform real-time calculation and analysis on the collected data, while offline computing mode can perform in-depth analysis on large batches of data. Correspondingly, the raw management data becomes more standardized after extraction, cleaning, and transformation by the data processing module 20, and further processed through streaming computing mode and offline computing mode to obtain event streams and analysis result sets, which are the processed management data. The key indicators include, but are not limited to, abnormal situation indicators, work order execution indicators, and intelligent terminal operation indicators. Defect identification results characterize the defects existing in the transmission line, including but not limited to defect type and defect location. The risk levels mentioned above are related to specific defects, equipment, and control measures. The higher the risk level, the more dangerous the defect is and the greater the negative impact it will have.

[0050] In this embodiment, the data acquisition terminal 10 first automatically acquires management data on the line itself and its surrounding environment and status information, and then sends the management data to the data processing module 20. The data acquisition terminal 10 can complete inspections based on acquired or preset intelligent inspection types and obtain the aforementioned management data. These inspection types include, but are not limited to: drone autonomous inspection (i.e., line inspection and testing, including visible light, infrared, 3D laser, etc.), robot inspection (i.e., line and cable inspection and testing, including visible light, infrared, and gas environment), online monitoring (i.e., intelligent terminal inspection plans such as video surveillance can directly penetrate to operation and map display, while video surveillance has the ability for manual operation and system autonomous operation based on pre-set locations), and status monitoring (including but not limited to infrared temperature measurement, conductor temperature measurement, lightning monitoring, load monitoring, fault location, and other monitoring data).

[0051] The data processing module 20 receives management data and performs cleaning, format conversion, and integration on data from different protocols to obtain processed management data. This processed management data is then stored in a pre-set database. Specifically, real-time processing of continuously generated data streams can be achieved through streaming computing. For example, the system establishes a continuously running computing task to analyze video streams captured by a camera frame by frame. If a crane, excavator, or a pre-set target is detected in the frame, an event record is generated. Streaming computing also includes real-time calculation of average values ​​and abrupt changes in data collected by sensors (one type of data acquisition terminal 10). More complex calculations can also be performed on large batches of data already stored in the database through offline computing. For example, a computing task can be started, calling a pre-set deep learning model to perform batch analysis on all images collected within a certain time period, identifying images with pre-set defects, and so on. In summary, processed management data can be obtained through streaming computing and offline computing.

[0052] Furthermore, the operation support module 30 generates key indicators based on the processed management data. The types of indicators can be set by relevant technical personnel according to actual needs. For example, work order execution indicators are determined based on the completion status of work orders; abnormal situation indicators can be generated based on monitored abnormal situations; and planned execution indicators can be determined based on the online working status of various terminals, and so on. The above defect identification results can be used by the operation support module 30 to call preset defect identification services. For example, after a batch of new data is entered into the database, the new data can be identified to determine the location, type, and defective equipment, and this information is stored in a structured manner to form formal defect identification results.

[0053] Then, the risk level can be determined based on the defect identification results and key indicators. The risk level is mainly determined based on the defect identification results, while key indicators are used to assist in the judgment. For example, if the defect category is detected as "broken strand of wire", the degree of negative impact can be determined by combining its location, equipment load, and the planned execution indicators of the area. Thus, the system automatically judges the risk level of the defect. The risk level is related to specific defects, equipment, areas, system control and other factors. The risk level can be determined by preset mapping data or by a well-trained neural network.

[0054] Finally, the risk level and defect identification results can be displayed through the preset terminal display module. For example, different types of risk levels can be marked with icons of different colors on a two-dimensional or three-dimensional map, and their associated defect identification results can be indicated.

[0055] The following is a complete execution flow in one embodiment, for reference only:

[0056] 1. Automatically generate a "drone inspection plan" based on manual input or preset plan management functions.

[0057] 2. The task is assigned to at least one specific drone (i.e., the data acquisition terminal 10 mentioned above), and the drone flies along the predetermined route and takes pictures.

[0058] 3. The images are transmitted to the data processing module 20 and stored in the preset database. The data processing module 20 enables offline computing mode and analyzes all images based on the preset analysis model to obtain the processed management data.

[0059] 4. The operation support module 30 determines key indicators based on the processed management data, such as planned completion indicators and defect discovery indicators, and determines the defect identification results. For example, if n insulator defect records are found, these n defect identification results are recorded.

[0060] 5. The operation support module 30 determines the risk level based on the defect identification results and key indicators, and displays it on the terminal display module. For example, it can mark n defects on a map and attach risk suggestions, close-up photos of the defects, defect types, etc., to facilitate timely repair of defects.

[0061] This application's embodiments realize a complete process from planning, data collection, analysis, decision-making, and presentation, greatly reducing the workload of manual inspection and data processing. Furthermore, this application can aggregate data collection results from multiple terminals, thereby obtaining more comprehensive and accurate analysis and identification results. This application also converts discrete defects into quantifiable risk levels, providing more intuitive calculation results and facilitating the optimization of operation and maintenance strategies.

[0062] In one exemplary embodiment, the system further includes an Internet of Things (IoT) module, which comprises a data acquisition submodule and a communication submodule:

[0063] The data acquisition submodule is used to acquire management data collected by the data acquisition terminal;

[0064] The communication submodule is used to send management data to the data processing module.

[0065] In this embodiment, the acquisition submodule is an edge node that connects to the data acquisition terminal via wired or wireless means. It actively reads or receives raw management data pushed by the data acquisition terminal. To achieve this effect, the IoT module usually has multiple communication protocol libraries built in. When the raw byte stream is received, the module performs protocol parsing according to the preset terminal type and restores it into meaningful management data.

[0066] Furthermore, the management data is sent to the upper-level data processing module through the communication submodule.

[0067] Through the embodiments of this application, by integrating multiple protocol parsing capabilities, terminal devices from different manufacturers, models, and communication methods can be connected to the system, avoiding data silos. Furthermore, this application uses an IoT module to parse and encapsulate data at the data source, converting messy and raw management data into data with a unified format before sending it to the data processing module, thus reducing the subsequent computational burden.

[0068] In one exemplary embodiment, the system further includes a cloud platform module;

[0069] The cloud platform module provides cloud resources to the data management system. These resources include computing resources, storage resources, network resources, and microservice management resources. The computing resources support the streaming and offline computing of the data processing module; the storage resources provide data storage capabilities for the data management system; the network resources provide data transmission capabilities for the data management system; and the microservice management resources support the operation support module in calculating key indicators, identifying and handling defects, and determining and displaying risk levels.

[0070] Cloud resources are standardized service units abstracted and provided externally by the cloud platform module. The data management system does not need to know the specific model, location, or maintenance of physical devices; it can call these cloud resources through APIs. Specifically, cloud resources include computing resources, storage resources, network resources, and microservice management resources. These four types of resources jointly support the functions of different modules in the system.

[0071] Specifically, computing resources are provided in the form of cloud servers or container clusters to support streaming and offline computing in the data processing module. For example, one or more container clusters can be created on the cloud platform, and streaming computing tasks can be deployed on the clusters to achieve efficient processing of the collected real-time data streams. Similarly, batch processing computing clusters with parallel computing capabilities can be created on the cloud platform. When there are offline tasks, the cloud platform can start the cluster on demand and allocate CPU and GPU resources for offline computing.

[0072] The aforementioned storage resources refer to virtualized persistent data storage space, used to provide data storage capabilities for data management systems.

[0073] The aforementioned network resources refer to virtualized network components, including but not limited to private clouds, subnets, firewalls, etc. A virtual private cloud can be created in the cloud platform as a logically isolated network environment. Different subnets can be created for data processing modules, operation support modules, etc. Routing tables and internal load balancers can be configured to ensure secure, high-speed, and stable intranet communication between modules (such as from the IoT module to the data processing module, and from the data processing module to the operation support platform).

[0074] The aforementioned microservice management resources are used to support the operation and management of various business computing tasks in the runtime support module. For example, each sub-module in the runtime support module can be encapsulated as an independent Docker container image, and the microservice management resources are responsible for the automated deployment and fault self-healing of these containers.

[0075] Through the embodiments of this application, the cloud platform module provides mechanisms such as module deployment, load balancing, data backup, service health checks and automatic recovery, which ensure the continuous and stable operation of core businesses (such as real-time monitoring and risk monitoring) and greatly improve the reliability of the system.

[0076] In one exemplary embodiment, the data acquisition terminal includes a visualization-type monitoring terminal, a status monitoring terminal, a drone monitoring terminal, a robot monitoring terminal, and a behavior monitoring terminal.

[0077] In this embodiment, the data acquisition terminal includes various types of terminals such as visual monitoring, status monitoring, drones, robots, and behavior monitoring. Specifically, the visual monitoring terminal can realize anti-icing, forest fire prevention, external damage prevention, and important crossing monitoring. The status monitoring terminal can realize conductor temperature measurement, tower tilt monitoring, pollution device monitoring, lightning arrester monitoring, micro-meteorology monitoring, distributed fault location monitoring, tension sensor monitoring, traveling wave network master station monitoring, slope monitoring, lightning monitoring device monitoring, galloping monitoring, and cable monitoring (partial discharge, circulating current, etc.). The drone terminal includes multi-rotor, fixed-wing, lidar pod, oblique photography pod, multi-photoelectric pod, and intelligent helipad. The robot terminal includes cable tunnel robot and overhead crane robot. The behavior monitoring terminal includes intelligent safety helmet, on-site control ball, video emergency monitoring device, on-site law enforcement recorder, and aerial electronic fence (BeiDou).

[0078] In one exemplary embodiment, the operation support module includes a key metrics submodule;

[0079] The key metrics submodule is used to generate key metrics based on the processed management data. Key metrics include anomaly indicators, work order execution indicators, plan execution indicators, terminal operation indicators, and defect and potential hazard indicators.

[0080] The key metrics submodule is used to perform statistics and calculations based on the processed business data according to predefined rules, and generate corresponding key metrics.

[0081] This application provides specific key indicators, including abnormal situation indicators, work order execution indicators, plan execution indicators, terminal operation indicators and defect and hidden danger indicators. Among them, the abnormal situation indicators reflect the number and level of abnormal events that occur in the transmission line and operating environment. The abnormal situation indicators include: (1) Video monitoring displays video monitoring alarm information, including multiple voltage level alarm numbers, which can be displayed differently according to the permissions of different maintenance personnel in the company, can be classified and located to two-dimensional and three-dimensional maps, accurate to specific lines and towers, can view alarm photos and historical photos, and can operate the camera.

[0082] (2) Status monitoring displays status monitoring alarm information, including alarm numbers at multiple voltage levels. It can display different information according to the permissions of different maintenance personnel in the company. It can be classified and located on two-dimensional and three-dimensional maps, accurate to specific lines and towers. Alarm information data can be viewed.

[0083] (3) Fault alarm display: Fault alarm information, including multiple voltage level alarm numbers, can be displayed differently according to the permissions of different maintenance personnel in the company, can be classified and linked with two-dimensional and three-dimensional maps, accurate to specific line sections and towers, can display relevant information such as trip distance measurement, can operate the camera if there is a camera, can view the distribution of on-site workers and warehouse distribution (view tool information).

[0084] (4) Operation alarm display load and real-time power grid risk alarm information, including alarm numbers at multiple voltage levels. It can display different information according to the permissions of different maintenance personnel in the company. It can be linked with two-dimensional and three-dimensional maps respectively, accurate to specific lines, and can display load-related information.

[0085] The above-mentioned work permit execution indicators reflect the execution process, compliance, and completion status of on-site work permits. In practical applications, the work permit execution indicators can be determined through the preset work permit management table in the data processing module. These indicators include, but are not limited to, the total number of live-line work permits and emergency repair work permits, the number of executions, the number of non-standard work permits, the number of non-compliant work permits, the number of non-compliant work permits, the line on which the work permit was executed that day, the status of the work permit execution, and the risk points that need to be monitored.

[0086] The aforementioned plan execution indicators characterize the actual completion and timeliness of various pre-defined operation and maintenance work plans (such as inspections, testing, and troubleshooting). Plan execution indicators include, but are not limited to, the total number of annual, monthly, and weekly plans, the total number of uncompleted tasks, overdue completions, and overdue non-completions.

[0087] The aforementioned terminal operation indicators characterize the online status and availability of each data acquisition terminal in the system. These terminal operation indicators include, but are not limited to, the total number, offline number, and online rate of intelligent terminals such as drones, robots, video surveillance, and online monitoring; and can penetrate to the corresponding intelligent terminal operation, control, and equipment management modules.

[0088] The above defect and hidden danger indicators reflect the number, level and progress of identified equipment defects and external environmental hazards, including but not limited to: (1) Defect display: total number of defects, number of defects not eliminated, overdue completion, overdue non-completion, including multiple voltage levels, which can be displayed differently according to the different permissions of the company's maintenance personnel, reflecting the defect level (urgent, major, general, other) and classification (tower, conductor, hardware, insulator, etc.).

[0089] (2) Hazard display: total number of hazards, number of hazards not eliminated, number of hazards completed beyond the deadline, and number of hazards not completed beyond the deadline. It includes multiple voltage levels and can display different levels according to the permissions of different maintenance personnel in the company. It can reflect the hazard level (major, general) and classification (tree obstruction, external force, geological disaster, etc.).

[0090] In one exemplary embodiment, the data processing module includes a data bridging submodule and a data fetching submodule;

[0091] The data bridging submodule is used to convert the management data collected by each acquisition terminal into a preset standard data format;

[0092] The data scraping submodule is used to scrape preset expected data from outside the data management system.

[0093] In this embodiment, the data bridging submodule is used to convert management data directly collected by various data acquisition terminals into a preset standard data format. This preset standard data format is a unified data structure within the system that facilitates storage, processing, and analysis. Specifically, the data bridging submodule is deployed between the IoT platform module and the data processing module, continuously monitoring the data aggregated from the IoT platform. It has pre-built protocol driver libraries corresponding to various acquisition terminals. When a raw data packet is received, it first automatically matches the corresponding protocol parser based on the specific identifier or source of the data packet, parses and encapsulates the raw data for subsequent calculation, storage, and analysis.

[0094] The aforementioned data crawling submodule is used to crawl the required data from platforms and systems that are not part of the data management system. The expected data refers to the data that needs to be obtained from external sources in advance based on the needs of business analysis and decision-making.

[0095] In this embodiment, desired external data, such as weather forecasts from the meteorological bureau or geological disaster warnings from the natural resources department, is obtained through a preset interface via a data capture submodule. After obtaining the external data, the capture submodule also cleans and converts it to conform to the internally preset standard data format.

[0096] Through the embodiments of this application, based on the data bridging submodule, various types of terminal data are converted into a unified standard data format through unified protocol adaptation and format conversion, facilitating subsequent storage, analysis, and application, solving the problem of data silos, and realizing unified governance of heterogeneous data. Furthermore, this application proactively introduces key external data such as meteorological and power grid operation data through the data capture submodule, enabling the system to perform more comprehensive and accurate analysis and detection.

[0097] In one exemplary embodiment, the operation support module further includes an auxiliary decision-making submodule and a risk management submodule;

[0098] The decision support submodule is used to call the preset identification model and calculate the defect identification result based on the processed management data.

[0099] The risk management submodule is used to quantify the corresponding risk level by calling a preset risk assessment model based on the defect identification results.

[0100] The aforementioned recognition model refers to an artificial intelligence algorithm model that is pre-trained and deployed in the system for a specific analysis task, such as an image recognition model based on a deep convolutional neural network, a natural language processing model (used to parse inspection record text), and so on.

[0101] In this embodiment of the application, the above-mentioned auxiliary decision-making sub-model runs continuously as a microservice, and its triggering conditions include, but are not limited to: event-driven (such as sending an analysis task message to the sub-module when a batch of newly collected images are detected to be added to the database), plan-driven (such as automatically triggering a batch analysis task of newly added data in the past preset time period according to a preset period), etc.

[0102] After receiving the task, the above-mentioned auxiliary decision-making submodule determines the recognition model to be called according to the task type. For example, after acquiring a batch of images, it calls the corresponding image detection recognition model to perform inference on each image and outputs whether there are defects in the image, the location of the defects, the category, the confidence score, etc., thereby obtaining the above-mentioned defect recognition results.

[0103] A risk assessment model refers to a computational engine that embeds risk quantification rules, formulas, or algorithms. In one embodiment, the model is a rule-based assessment system, and in another embodiment, the model is a predictive model that uses historical data for calculation.

[0104] When the auxiliary decision-making submodule generates a new defect identification result, the risk management submodule is triggered. It not only reads the defect identification result itself, but also obtains information related to the defect through management queries, such as environmental data and equipment ledger information. Based on the defect identification result and the aforementioned defect-related information, it is input into the risk assessment model. The risk assessment model generates the corresponding risk level, which includes, but is not limited to, equipment risk (Levels I, II, III, and IV), power grid risk (Levels I, II, III, and IV), and operational risk (Extremely High, High, Medium, Low, and Acceptable). Each risk must be able to penetrate to the corresponding risk (penetrate to the risk management module). The last level is the risk control table, which enables intelligent generation of risk control measures and intelligent assessment of the implementation of control measures.

[0105] Through the embodiments of this application, the auxiliary decision-making submodule and the risk management submodule work together to transform the original data stream into a clear risk level, thereby realizing the quantification, classification and comparability of risks.

[0106] In one exemplary embodiment, the operation support module further includes a system management submodule;

[0107] The system management submodule is used to set the basic operating configuration of the data management system.

[0108] The basic operation configuration refers to the set of parameters, strategies, and rules used to ensure the normal operation of the system. It does not include specific transmission line monitoring data, but only includes metadata used to indicate the operation of the system.

[0109] In this embodiment, the system management submodule provides configuration capabilities to the administrator through a preset interface to obtain the basic operational configuration input by the administrator. The basic operational configuration includes, but is not limited to, user and permission configuration (i.e., defining users who can access the system and their executable operation scope / permissions), and business process and data rule configuration (i.e., defining the workflow, data standards, etc., for various operation and maintenance businesses, such as defining "wire damage cross-sectional area < 7%" as a general defect, and "wire damage cross-sectional area ≥ 7%" as a major defect, etc.).

[0110] The embodiments of this application enable flexible configuration and dynamic adjustment of business rules, thereby improving the adaptability of the system.

[0111] In one exemplary embodiment, the terminal display module includes a mobile display terminal and a fixed display terminal.

[0112] In this embodiment of the application, the ledger information, acquired videos, device status information, collected historical information, terminal information and PTZ operation information of each intelligent data acquisition terminal can also be displayed in categories. The above information can be displayed through the mobile display module.

[0113] The following is a preferred embodiment of a data management system for power transmission lines. Figure 2 This is a schematic diagram of the data management system for a transmission line in a preferred embodiment.

[0114] First, the system automatically acquires basic data such as inspection plan work orders, equipment ledgers, and quantified inspection standards through an external power grid management platform. Based on these plans, the system internally generates specific inspection tasks and automatically schedules the corresponding data acquisition terminals to execute them. For example, drones conduct autonomous inspections along preset routes and capture images; robots move in tunnels or along power lines to perform close-range inspections; fixed cameras monitor important sections for extended periods; and various sensors continuously collect data on line status and the environment, etc.

[0115] Secondly, each data acquisition terminal device uploads the massive amount of heterogeneous management data it collects to the data processing module. The data processing module extracts, cleans, and transforms the management data into a standardized format, and uses streaming computing to perform real-time analysis of the management data to quickly capture abnormal events. For batch data transmitted back by devices such as drones, offline computing is used for in-depth analysis and fine identification to obtain processed management data, which is then stored in a structured manner.

[0116] Secondly, the operation support module generates key indicators based on the processed management data. The types of indicators can be set by relevant technical personnel according to actual needs. For example, work order execution indicators are determined based on the completion status of work orders; abnormal situation indicators are generated based on monitored abnormal situations; and planned execution indicators are determined based on the online working status of various terminals, and so on. The above defect identification results can be used by the operation support module to call preset defect identification services. For example, after a batch of new data is entered into the database, the new data can be identified to determine the location, type, and defective equipment, and this information is stored in a structured manner to form formal defect identification results.

[0117] Furthermore, based on the processed management data, corresponding defect identification results are generated. Specifically, the auxiliary decision-making submodule in the system's operation support module calls the preset identification model and calculates the defect identification results based on the processed management data.

[0118] Furthermore, the defect identification results are transmitted to the risk management submodule, where the risk is quantified using a preset risk assessment model. Key indicators are used as auxiliary indicators for judgment. For example, if the defect category is detected as "broken strand in a conductor", the system determines the degree of negative impact by combining its location, equipment load, and the planned execution indicators of the area. Thus, the system automatically judges the risk level of the defect.

[0119] Finally, the risk level is displayed through the terminal presentation layer, and the raw data, process data (such as key indicator data), and result data (such as defect identification results and risk levels) are all stored. In one embodiment, all inspection data, confirmed defect data, and assessed risk data generated during this inspection operation can also be transmitted back to an external power grid management platform system, realizing a closed-loop business process from plan issuance to result feedback.

[0120] The following section provides a further introduction to each sub-module of the data management system:

[0121] The data acquisition terminals at the terminal layer include drones (mainly used for collecting images and videos), cameras, sensing terminals (including various sensors such as conductor temperature measurement and tower tilt, which collect physical state data), surveillance spheres (as mobile terminals, used for on-site operation recording and temporary monitoring), and quality inspection APPs (as mobile terminals, used for on-site operation recording and temporary monitoring).

[0122] The Internet of Things (IoT) module includes data acquisition devices, smart gateways, and communication devices. The data acquisition devices and smart gateways are mainly used to access and initially process terminal data at the edge, while the communication devices mainly provide network connectivity.

[0123] The big data computing module in the data processing module provides both streaming and offline computing modes to perform data analysis and preliminary preprocessing tasks. Unstructured databases, time-series databases, and relational databases are primarily used to provide optimal storage solutions for different types of data. For example, unstructured databases are mainly used to store images, videos, and other files; time-series databases are mainly used to store time-stamped sensor data; and relational databases are mainly used to store structured business relationship data such as equipment ledgers, inspection plans, and personnel information. Extraction, cleaning, transformation, and storage (i.e., storage in the corresponding databases) constitute the standardization preprocessing of the raw data. Data bridging refers to receiving different types of data transmitted from various terminals and converting the data into a unified format. Real-time data streaming and file transfer refer to reliably transmitting data to the data processing module in an adapted manner (streaming, batch transmission, etc.).

[0124] The cloud platform module primarily provides various computing resources, such as virtualized central processing units, graphics processing units, and memory. Storage resources offer diverse virtualized persistent data storage services, matching different types based on data characteristics. Network resources provide virtualized network components, building and managing the system's data communication plane. Component loading utilizes Docker, a containerization technology, to package the aforementioned business functional modules (such as key metrics and risk management) and their runtime environments into a lightweight, portable container image, ensuring consistency between development, testing, and runtime environments. Service governance includes a service registration and discovery center, where each microservice registers its network address upon startup; load balancing, at the microservice level, works in conjunction with the aforementioned network load balancing to evenly distribute external requests and internal service call traffic across multiple container instances of the same service, avoiding single-point overload; the API gateway serves as the system's sole external entry point; and the configuration center centrally and dynamically manages the application configurations of all microservices (such as database connection strings and risk calculation thresholds). When configuration changes are needed, there's no need to restart the service; changes can be made globally through the configuration center, greatly improving operational efficiency and system flexibility. Logs provide distributed log collection, storage, and query services. All operational and business logs generated by microservices, containers, and cloud resources are collected here for troubleshooting, performance analysis, and security auditing.

[0125] The key performance indicator (KPI) submodule within the operation support module is used to calculate and generate corresponding KPIs based on processed management data; the risk management submodule is used to quantify and assess the risk of defects and determine the risk level; the decision support submodule is used to call a preset identification model and calculate the defect identification result based on the processed management data; the intelligent production submodule is used to convert abstract operation and maintenance plans (such as conducting a detailed inspection of the XX line this month) into specific, executable plans and drive the data acquisition terminal to complete the operation, thereby obtaining the aforementioned management data; the system management submodule is used to manage user permissions, processes, and other basic configuration information; and the production information and application service modules are both used to provide comprehensive information queries and general service interfaces.

[0126] Finally, the terminal display module includes a mobile display terminal and a fixed display terminal.

[0127] Based on the same inventive concept, this application also provides a data management method for transmission lines to implement the data management system for transmission lines mentioned above. The solution provided by this method is similar to the implementation scheme described in the above system. Therefore, the specific limitations in one or more embodiments of the data management method for transmission lines provided below can be found in the limitations of the data management system for transmission lines described above, and will not be repeated here.

[0128] In one exemplary embodiment, such as Figure 3 As shown, a data management method for transmission lines is provided, including:

[0129] Step S310: Obtain management data corresponding to the transmission line;

[0130] Step S320: Process the management data using streaming computing mode and offline computing mode to obtain processed management data;

[0131] Step S330: Generate key indicators based on the processed management data, and calculate the defect identification results based on the key indicators and the processed management data;

[0132] Step S340: Determine the risk level based on the defect identification results, and display the risk level and defect identification results in the preset terminal display module.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement any of the data management methods for transmission lines described above.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the data management methods for transmission lines described above.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the data management methods for transmission lines described above.

[0136] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0137] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0138] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data management system for power transmission lines, characterized in that, The system includes a data acquisition terminal, a data processing module, and an operation support module: The data acquisition terminal is used to monitor the transmission lines and obtain management data corresponding to the transmission lines; The data processing module is used to pre-compute the management data through streaming computing mode and offline computing mode to obtain processed management data. The operation support module is used to generate key indicators and defect identification results based on the processed management data. The operation support module is also used to determine the risk level based on the defect identification results and key indicators, and to display the risk level and the defect identification results in a preset terminal display module.

2. The system according to claim 1, characterized in that, The system also includes an Internet of Things (IoT) module, which comprises a data acquisition submodule and a communication submodule. The acquisition submodule is used to acquire the management data collected by the data acquisition terminal; The communication submodule is used to send the management data to the data processing module.

3. The system according to claim 1, characterized in that, The system also includes a cloud platform module; The cloud platform module is used to provide cloud resources to the data management system. The cloud resources include computing resources, storage resources, network resources, and microservice management resources. The computing resources are used to support the streaming computing and offline computing of the data processing module. The storage resources are used to provide data storage capabilities for the data management system. The network resources are used to provide data transmission capabilities for the data management system. The microservice management resources are used to support the operation support module in performing the calculation of key indicators, defect identification and processing, and determination and display of risk levels.

4. The system according to any one of claims 1 to 3, characterized in that, The data acquisition terminals include visual monitoring terminals, status monitoring terminals, drone monitoring terminals, robot monitoring terminals, and behavior monitoring terminals.

5. The system according to any one of claims 1 to 3, characterized in that, The operational support module includes a key indicator sub-module; The key indicator submodule is used to generate key indicators based on the processed management data. The key indicators include abnormal situation indicators, work order execution indicators, plan execution indicators, terminal operation indicators, and defect and potential hazard indicators.

6. The system according to any one of claims 1 to 3, characterized in that, The data processing module includes a data bridging submodule and a data capture submodule; The data bridging submodule is used to convert the management data collected by each acquisition terminal into a preset standard data format; The data capture submodule is used to capture preset desired data from outside the data management system.

7. The system according to any one of claims 1 to 3, characterized in that, The operation support module also includes an auxiliary decision-making submodule and a risk management submodule; The auxiliary decision-making submodule is used to call a preset identification model and calculate the defect identification result based on the processed management data; The risk management submodule is used to quantify the corresponding risk level by calling a preset risk assessment model based on the defect identification results.

8. The system according to any one of claims 1 to 3, characterized in that, The operation support module also includes a system management submodule; The system management submodule is used to set the basic operating configuration of the data management system.

9. The system according to any one of claims 1 to 3, characterized in that, The terminal display module includes a mobile display terminal and a fixed display terminal.

10. A data management method for transmission lines, characterized in that, The data management system as described in any one of claims 1 to 9 includes: Obtain the management data corresponding to the transmission line; The management data is processed using streaming computing mode and offline computing mode to obtain processed management data; Key indicators are generated based on the processed management data, and defect identification results are calculated based on the key indicators and the processed management data. The risk level is determined based on the defect identification results, and the risk level and the defect identification results are displayed in a preset terminal display module.

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