Novel digital management label system for physical equipment of power grid

Abstract

The invention belongs to the technical field of power grid material management, and discloses a novel digital management label system for power grid physical equipment. The label comprises a label information management module, a data acquisition and preprocessing module, a label classification and clustering module, a label storage and relational database module, a data analysis and prediction module, a visual display module and a non-contact management and Internet of Things integration module. According to the invention, through cooperation of the label classification and clustering module and the label storage and relational database module, significant advantages are brought to a digital management label system of power grid physical equipment; in the data level, the data acquisition and preprocessing unit provides accurate and consistent data, and after the data is utilized in the subsequent process, the data quality of the label storage and management unit is guaranteed, and a solid foundation is laid for a label library and a relational database; in the aspect of label management, a label classification management unit optimizes label management and creates a classification system.

Description

Technical Field

[0001] This invention belongs to the field of power grid material management technology, specifically a new type of digital management label system for physical power grid equipment. Background Technology

[0002] The new digital management tag for power grid physical equipment is a digital management tool integrating advanced information processing and Internet of Things (IoT) technologies. It enables full lifecycle management, visualized control, and efficient management of power grid physical equipment, providing strong support for the safe, stable, and efficient operation of the power grid. Traditional power grid physical equipment management models have several shortcomings: First, due to the frequent use of physical equipment, the labels and nameplates on them are often easily damaged and have a short lifespan, causing inconvenience for material management personnel to review historical entry and exit records on-site; second, when faced with a large number of similar physical equipment or numerous accessories for large sets of equipment, material management personnel spend a considerable amount of time identifying and distinguishing the integrity of the equipment on-site. Summary of the Invention

[0003] The purpose of this invention is to provide a novel digital management tagging system for physical power grid equipment to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a novel digital management tag system for physical power grid equipment, the tag including a tag information management module, a data acquisition and preprocessing module, a tag classification and clustering module, a tag storage and relational database module, a data analysis and prediction module, a visualization module, and a contactless management and Internet of Things integration module;

[0005] The tag information management module is responsible for storing and managing the basic information of tags, including tag ID, name, type, and creation method; the tag ID adopts a two-segment encoding method for easy retrieval and classification management;

[0006] The data acquisition and preprocessing module collects real-time status data of power grid equipment through sensors and PLC devices, and performs preprocessing, such as data cleaning and formatting, to provide accurate basic data for subsequent analysis.

[0007] The tag classification and clustering module mainly classifies and clusters tags based on the device's basic information, operating information, and status information to form a deep tag system;

[0008] The tag storage and relational database module establishes a relational database between data tags and the control objects of power grid equipment based on the tag library and tag basic information, and supports the storage and marking of dynamic tags;

[0009] The data analysis and prediction module utilizes big data analysis and machine learning technologies to deeply mine the collected data, predict equipment failures, optimize operation and maintenance strategies, and generate alarm information.

[0010] The visualization module uses a web platform or mobile application to match and extract device information, configurations, and dictionaries, enabling multi-dimensional display and analysis, and improving asset management efficiency.

[0011] The contactless management and IoT integration module utilizes RFID technology to achieve contactless management of equipment, and combines it with IoT technology to achieve real-time perception and control of the equipment throughout its entire lifecycle.

[0012] Preferably, the tag information management module includes a data storage and retrieval unit, an encoding generation and management unit, a data update and maintenance unit, and an access control and management unit.

[0013] The data storage and retrieval unit is responsible for comprehensively storing the basic information of the tags, covering key data such as tag ID, name, type, and creation method. This unit has efficient data retrieval capabilities, especially supporting the rapid location of information through two-segment coded tag IDs. The encoding generation and management unit is responsible for generating and managing two-segment tag IDs. By designing algorithms or rules, it ensures the uniqueness of the IDs and compliance with encoding standards. At the same time, it tracks the usage of IDs to effectively avoid duplicate generation and ensure the accuracy and efficiency of tag management.

[0014] The data update and maintenance unit is responsible for handling requests to modify, add, and delete tag information, ensuring data consistency and integrity. During concurrent updates, the unit can effectively handle conflicts. In addition, it provides data backup and recovery functions, providing comprehensive security for the storage and management of tag information.

[0015] The access control and management unit is responsible for user authentication and authorization, strictly controlling access permissions to tag information, and only authorizing users to perform operations such as querying, modifying or deleting. At the same time, this unit records access logs to provide strong support for auditing and troubleshooting.

[0016] Preferably, the data acquisition and preprocessing module includes a data acquisition unit, a data preprocessing unit, a data verification and validation unit, and a data storage and management unit;

[0017] The data acquisition unit uses sensors, PLCs and other devices to acquire real-time status data of power grid equipment, ensuring the real-time nature, accuracy and integrity of the data, and providing a reliable information foundation for the system;

[0018] The data preprocessing unit is responsible for cleaning the raw data, removing noise, anomalies and duplicates, and performing formatting to ensure data quality and meet the needs of subsequent analysis.

[0019] The data verification and validation unit is responsible for ensuring that the preprocessed data meets the expected format and quality standards, and for verifying its integrity and consistency, so as to ensure the accuracy and reliability of the data and lay a solid foundation for subsequent analysis.

[0020] The data storage and management unit is responsible for properly storing the pre-processed and verified data in a database or data warehouse, and provides a convenient data access interface to provide a fast and accurate data acquisition channel for subsequent analysis;

[0021] The data acquisition unit provides the data preprocessing unit with real-time, accurate, and complete power grid equipment status data; after the preprocessing unit cleans the data, the data verification and validation unit ensures data quality; the data storage and management unit stores the verified data, providing fast access for subsequent analysis; each unit is interconnected to ensure the continuity and quality of the data flow.

[0022] Preferably, the tag classification and clustering module includes a data acquisition and preprocessing unit, a tag classification management unit, a cluster analysis unit, a fuzzy reasoning and fault probability calculation unit, and a data storage and management unit;

[0023] The data acquisition and preprocessing unit is responsible for collecting basic information, operational information, and status information from various devices in real time, and performing preprocessing, including data cleaning, formatting, and normalization, to provide an accurate and consistent data foundation for subsequent classification and clustering analysis.

[0024] The tag classification management unit scientifically classifies and manages tags based on device information characteristics and business needs, creates a classification system, specifies hierarchical attributes, supports tag modification and deletion, improves tag search and usage efficiency, and meets business management needs.

[0025] The clustering analysis unit uses clustering algorithms to perform clustering analysis on the preprocessed device information data, grouping similar device information into one category to form a deep labeling system.

[0026] Based on cluster analysis, the fuzzy reasoning and fault probability calculation unit uses fuzzy reasoning to enrich the label connotation and combines fault probability to assess equipment risk, providing accurate decision support for equipment maintenance and management and improving the accuracy and practicality of the labels.

[0027] The data storage and management unit is responsible for storing the classified and clustered label and device information data, and provides an efficient data access interface to support subsequent analysis applications;

[0028] The data acquisition and preprocessing unit provides accurate data for the label classification management unit and the cluster analysis unit; the label classification management unit optimizes label management and supports cluster analysis; the cluster analysis unit processes data and provides deep labels for the fuzzy inference and fault probability calculation unit; the fuzzy inference and fault probability calculation unit assesses risk based on the clustering results; the data storage and management unit stores all information, supports subsequent analysis, and ensures data security.

[0029] Preferably, the tag storage and relational database module includes a tag library management unit, a basic information management unit, a relational database construction unit, and a dynamic tag management unit;

[0030] The tag library management unit is responsible for creating, storing, and updating tags, ensuring the accuracy and completeness of tag information. The basic information management unit is responsible for storing basic information about the control objects of power grid equipment, such as equipment type, location, and parameters, providing basic data for tag association. The relational database construction unit, based on the tag library and basic information, constructs the association between data tags and control objects of power grid equipment, realizing dynamic storage and marking of tags. The dynamic tag management unit supports the dynamic addition, modification, and deletion of tags, ensuring that the relational database can reflect the latest status of control objects of power grid equipment in real time.

[0031] Preferably, the data analysis and prediction module includes a data acquisition and preprocessing unit, a big data analysis unit, a machine learning prediction unit, and an alarm information generation and notification unit.

[0032] The data acquisition and preprocessing unit is responsible for collecting data from multiple data sources and performing preprocessing such as cleaning, formatting, and noise reduction to ensure data quality and consistency and enhance the value of data applications.

[0033] The big data analysis unit uses big data technology and algorithms to deeply mine preprocessed data, revealing hidden patterns, trends and correlations, providing a solid foundation for accurate prediction and scientific decision-making.

[0034] The machine learning prediction unit uses machine learning algorithms to build prediction models, predict equipment failures, optimize operation and maintenance, evaluate performance and fine-tune it, and improve prediction accuracy.

[0035] The alarm information generation and notification unit generates alarms based on prediction results and thresholds, and promptly notifies relevant personnel via email, SMS, and push notifications. It also supports the recording and querying of alarm information for easy subsequent analysis and tracking.

[0036] The data acquisition and preprocessing unit provides high-quality data to the big data analysis unit; the big data analysis unit mines the value of the data, providing a foundation for the machine learning prediction unit; the machine learning prediction unit builds models to predict faults, triggering the alarm information generation and notification unit; the alarm unit promptly notifies relevant personnel and records information for subsequent analysis. All units work together to form a closed loop.

[0037] Preferably, the visualization module refers to the efficient and accurate matching and intelligent extraction of equipment information, configuration details, and preset dictionaries through a web platform or mobile application. This process not only ensures the comprehensiveness and accuracy of the data, but also greatly enriches the information foundation of the asset management system. With the help of advanced data processing technology, it realizes multi-dimensional display and in-depth analysis of equipment information, including status monitoring and trend prediction, providing managers with an intuitive and detailed overview of asset status.

[0038] Preferably, the contactless management and IoT integration module includes an RFID data acquisition unit, an IoT data integration unit, an equipment status monitoring unit, a full lifecycle management unit, and a user interaction and display unit;

[0039] The RFID data acquisition unit uses RFID technology to read the RFID tag information of the device, realizing non-contact identification and data acquisition of the device;

[0040] Implementation method: Deploy RFID readers and antennas to ensure that devices within the coverage area can be accurately read. At the same time, adopt appropriate RFID protocols and algorithms to improve the accuracy and reliability of data collection.

[0041] The IoT data integration unit integrates and consolidates the device information acquired by the RFID data acquisition unit with other data sources in the IoT.

[0042] Implementation method: Use an IoT platform or middleware to achieve unified access, processing and forwarding of data. At the same time, use data fusion and correlation analysis technology to integrate data from different sources to form a complete data view of the entire life cycle of the device.

[0043] The device status monitoring unit monitors and analyzes the real-time status of the device based on the data provided by the Internet of Things data integration unit.

[0044] Implementation method: Real-time monitoring technology and algorithms are used to monitor and analyze the operating status and working parameters of the equipment. At the same time, early warning and alarm mechanisms are set up so that when the equipment status is abnormal, an alarm is issued in a timely manner and corresponding handling measures are taken.

[0045] The full lifecycle management unit combines RFID technology and IoT data to manage and track the entire lifecycle of the equipment;

[0046] Implementation method: Establish a full lifecycle management database for equipment to record detailed equipment information, usage history, and maintenance records. At the same time, use data analysis technology to mine and analyze the full lifecycle data of the equipment to provide support for equipment management and decision-making.

[0047] The user interaction and display unit is responsible for providing a user-friendly interface and display method, enabling users to easily view device information, status monitoring results, and full lifecycle management data.

[0048] Implementation: Use web front-end technology or mobile application technology to build a responsive user interface; at the same time, use data visualization technology to display complex data in the form of charts, reports and other forms to improve the readability and understanding of the data.

[0049] Preferably, the specific steps for using the management tag are as follows: The system achieves intelligent management of power grid equipment through multi-module collaboration; in the initialization phase, functional modules are deployed, a tag library and permission system are built, and basic equipment information is entered through the tag information management module; in the data acquisition phase, sensors and PLC devices are deployed to collect operating status data in real time, which is then cleaned, verified, and stored; the tag classification and clustering analysis module creates a classification system based on equipment characteristics, applies clustering algorithms to generate cluster tags, and assesses risk levels through fuzzy reasoning to dynamically optimize tag attributes; the tag library and relational database support the addition, deletion, and modification of tags and the synchronous update of equipment information, building many-to-many relationships; the data analysis and prediction module integrates multi-source data, mines operating patterns, predicts fault probabilities, and triggers alarms; the visualization module presents equipment status in the form of a GIS map, supporting interactive queries and drill-down analysis; non-contact management uses RFID technology to read equipment information, the IoT integration unit integrates data to form a full lifecycle archive, and the status monitoring unit detects anomalies in real time and pushes alarms, realizing full-process equipment management.

[0050] The beneficial effects of this invention are as follows:

[0051] 1. This invention brings significant advantages to the digital management tag system for physical equipment in the power grid through the coordinated operation of the tag classification and clustering module and the tag storage and relational database module. At the data level, the data acquisition and preprocessing unit provides accurate and consistent data, which, after being utilized in subsequent processes, ensures the data quality of the tag storage and management unit, laying a solid foundation for the tag library and relational database. In terms of tag management, the tag classification management unit optimizes tag management and creates classification systems, which not only facilitates cluster analysis and data processing but also provides rich resources for the tag library management unit, making tag creation, storage, and updates easier. The deep tag system formed by the cluster analysis unit assists the fuzzy reasoning and fault probability calculation units in assessing risks, and these deep tags and related data are properly stored to support subsequent analysis. They also provide key data for the relational database construction unit, helping to build accurate relationships. The flexibility of the dynamic tag management unit can reflect tag changes in a timely manner, ensuring the system is accurate and up-to-date, meeting business needs. At the same time, the combined efforts of the two modules enable efficient storage and management of tag and equipment basic information and their relationships, facilitating rapid querying and retrieval, and significantly improving system operating efficiency and decision support capabilities.

[0052] 2. This invention, through full lifecycle management combined with RFID and IoT data, helps enterprises comprehensively track equipment status and optimize equipment management decisions. The user interaction and display unit greatly enhances the user experience through a user-friendly interface and visualized data display, making complex data clear at a glance. In addition, the integration of contactless management and IoT modules accelerates the pace of enterprise digital transformation, not only improving operational efficiency and management level, but also effectively reducing operating costs. The application of these innovative technologies not only enhances the competitiveness of enterprises, but also brings higher operational efficiency and lower maintenance costs, and is an important driving force for promoting the continuous development and innovation of enterprises. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] like Figure 1As shown, this embodiment of the invention provides a novel digital management tag system for physical power grid equipment. The tag includes a tag information management module, a data acquisition and preprocessing module, a tag classification and clustering module, a tag storage and relational database module, a data analysis and prediction module, a visualization module, and a contactless management and Internet of Things integration module.

[0056] The tag information management module is responsible for storing and managing the basic information of tags, including tag ID, name, type, and creation method; the tag ID adopts a two-segment encoding method for easy retrieval and classification management;

[0057] The data acquisition and preprocessing module collects real-time status data of power grid equipment through sensors, PLCs and other devices, and performs preprocessing, such as data cleaning and formatting, to provide accurate basic data for subsequent analysis.

[0058] The tag classification and clustering module mainly classifies and clusters tags based on the equipment's basic information, operational information, and status information to form a deep tag system. For example, it enriches the connotation of the tags through fuzzy reasoning and fault probability calculation.

[0059] The tag storage and relational database module establishes a relational database between data tags and the control objects of power grid equipment based on the tag library and tag basic information, and supports the storage and marking of dynamic tags;

[0060] The data analysis and prediction module utilizes big data analysis and machine learning technologies to deeply mine the collected data, predict equipment failures, optimize operation and maintenance strategies, and generate alarm information.

[0061] The visualization module uses a web platform or mobile application to match and extract device information, configurations, and dictionaries, enabling multi-dimensional display and analysis, and improving asset management efficiency.

[0062] The contactless management and IoT integration module utilizes RFID technology to achieve contactless management of equipment, and combines it with IoT technology to achieve real-time perception and control of the equipment throughout its entire lifecycle.

[0063] The tag information management module includes a data storage and retrieval unit, an encoding generation and management unit, a data update and maintenance unit, and an access control and management unit.

[0064] The data storage and retrieval unit is responsible for comprehensively storing the basic information of the tags, covering key data such as tag ID, name, type, and creation method. This unit has efficient data retrieval capabilities, especially supporting the rapid location of information through the two-segment encoded tag ID. To improve storage and retrieval efficiency, the unit makes full use of database indexing and caching technologies to ensure rapid response and performance optimization of information retrieval, providing a solid guarantee for the rapid access and management of tag information.

[0065] The encoding generation and management unit is responsible for generating and managing two-segment tag IDs. It ensures the uniqueness of the IDs and compliance with encoding standards through algorithm or rule design. At the same time, it tracks ID usage to effectively avoid duplicate generation and ensure the accuracy and efficiency of tag management.

[0066] The data update and maintenance unit is responsible for handling requests to modify, add, and delete tag information, ensuring data consistency and integrity. During concurrent updates, the unit can effectively handle conflicts. In addition, it provides data backup and recovery functions, providing comprehensive security for the storage and management of tag information.

[0067] The access control and management unit is responsible for user authentication and authorization, strictly controlling access permissions to tag information, and only authorizing users to perform operations such as querying, modifying or deleting. At the same time, this unit records access logs to provide strong support for auditing and troubleshooting, ensuring the safe and stable operation of the system.

[0068] The data acquisition and preprocessing module includes a data acquisition unit, a data preprocessing unit, a data verification and validation unit, and a data storage and management unit.

[0069] The data acquisition unit uses sensors, PLCs and other devices to acquire real-time status data of power grid equipment, ensuring the real-time nature, accuracy and integrity of the data, and providing a reliable information foundation for the system;

[0070] The data preprocessing unit is responsible for cleaning the raw data, removing noise, anomalies and duplicates, and performing formatting to ensure data quality and meet the needs of subsequent analysis.

[0071] The data verification and validation unit is responsible for ensuring that the preprocessed data meets the expected format and quality standards, and for verifying its integrity and consistency, so as to ensure the accuracy and reliability of the data and lay a solid foundation for subsequent analysis.

[0072] The data storage and management unit is responsible for properly storing the pre-processed and verified data in a database or data warehouse, and provides a convenient data access interface to provide a fast and accurate data acquisition channel for subsequent analysis;

[0073] The data acquisition unit provides the data preprocessing unit with real-time, accurate, and complete power grid equipment status data; after the preprocessing unit cleans the data, the data verification and validation unit ensures data quality; the data storage and management unit stores the verified data, providing fast access for subsequent analysis; each unit is interconnected to ensure the continuity and quality of the data flow.

[0074] The tag classification and clustering module includes a data acquisition and preprocessing unit, a tag classification management unit, a cluster analysis unit, a fuzzy reasoning and fault probability calculation unit, and a data storage and management unit.

[0075] The data acquisition and preprocessing unit is responsible for collecting basic information, operational information, and status information from various devices in real time, and performing preprocessing, including data cleaning, formatting, and normalization, to provide an accurate and consistent data foundation for subsequent classification and clustering analysis.

[0076] The tag classification management unit scientifically classifies and manages tags based on device information characteristics and business needs, creates a classification system, specifies hierarchical attributes, supports tag modification and deletion, improves tag search and usage efficiency, and meets business management needs.

[0077] The clustering analysis unit uses clustering algorithms (such as K-means clustering, DBSCAN, etc.) to perform clustering analysis on the preprocessed device information data, grouping similar device information into one category to form a deep labeling system. The clustering analysis unit needs to be able to process large-scale data and has efficient computing power.

[0078] Based on cluster analysis, the fuzzy reasoning and fault probability calculation unit uses fuzzy reasoning to enrich the label connotation and combines fault probability to assess equipment risk, providing accurate decision support for equipment maintenance and management and improving the accuracy and practicality of the labels.

[0079] The data storage and management unit is responsible for storing the classified and clustered label and device information data, providing an efficient data access interface to support subsequent analysis applications, and has data backup and recovery functions to ensure data integrity and security.

[0080] The data acquisition and preprocessing unit provides accurate data to the label classification management unit and the cluster analysis unit; the label classification management unit optimizes label management and supports cluster analysis; the cluster analysis unit processes the data and provides deep labels to the fuzzy inference and fault probability calculation unit; the fuzzy inference and fault probability calculation unit assesses risk based on the clustering results; the data storage and management unit stores all information, supports subsequent analysis, and ensures data security. All units work closely together to form a complete data processing chain.

[0081] The tag storage and relational database module includes a tag library management unit, a basic information management unit, a relational database construction unit, and a dynamic tag management unit.

[0082] The tag library management unit is responsible for creating, storing, and updating tags, ensuring the accuracy and completeness of tag information. The basic information management unit is responsible for storing basic information about the control objects of power grid equipment, such as equipment type, location, and parameters, providing basic data for tag association. The relational database construction unit, based on the tag library and basic information, constructs the association between data tags and control objects of power grid equipment, realizing dynamic storage and marking of tags. The dynamic tag management unit supports the dynamic addition, modification, and deletion of tags, ensuring that the relational database can reflect the latest status of control objects of power grid equipment in real time.

[0083] The data analysis and prediction module includes a data acquisition and preprocessing unit, a big data analysis unit, a machine learning prediction unit, and an alarm information generation and notification unit.

[0084] The data acquisition and preprocessing unit is responsible for collecting data from multiple data sources and performing preprocessing such as cleaning, formatting, and noise reduction to ensure data quality and consistency and enhance the value of data applications.

[0085] The big data analysis unit uses big data technology and algorithms to deeply mine preprocessed data, revealing hidden patterns, trends and correlations, providing a solid foundation for accurate prediction and scientific decision-making.

[0086] The machine learning prediction unit uses machine learning algorithms to build prediction models, predict equipment failures, optimize operation and maintenance, evaluate performance and fine-tune it, and improve prediction accuracy.

[0087] The alarm information generation and notification unit generates alarms based on prediction results and thresholds, and promptly notifies relevant personnel via email, SMS, and push notifications. It also supports the recording and querying of alarm information for easy subsequent analysis and tracking.

[0088] The data acquisition and preprocessing unit provides high-quality data to the big data analysis unit; the big data analysis unit mines the value of the data, providing a foundation for the machine learning prediction unit; the machine learning prediction unit builds models to predict faults, triggering the alarm information generation and notification unit; the alarm unit promptly notifies relevant personnel and records information for subsequent analysis. All units work together to form a closed loop.

[0089] The visualization module, implemented via a web platform or mobile application, efficiently and intelligently matches and extracts equipment information, configuration details, and a pre-defined dictionary. This process not only ensures the comprehensiveness and accuracy of the data but also significantly enriches the information foundation of the asset management system. Leveraging advanced data processing technology, it enables multi-dimensional display and in-depth analysis of equipment information, including status monitoring and trend prediction, providing managers with an intuitive and detailed overview of asset status. This innovative model not only optimizes management processes but also significantly improves the efficiency of asset management and the scientific nature of decision-making, laying a solid foundation for the company's sustainable development and enhanced competitiveness.

[0090] The contactless management and IoT integration module includes an RFID data acquisition unit, an IoT data integration unit, an equipment status monitoring unit, a full lifecycle management unit, and a user interaction and display unit.

[0091] The RFID data acquisition unit uses RFID technology to read the RFID tag information of the device, realizing non-contact identification and data acquisition of the device;

[0092] Implementation method: Deploy RFID readers and antennas to ensure that devices within the coverage area can be accurately read. At the same time, adopt appropriate RFID protocols and algorithms to improve the accuracy and reliability of data collection.

[0093] The IoT data integration unit integrates and consolidates the device information acquired by the RFID data acquisition unit with other data sources in the IoT (such as sensor data, device status data, etc.).

[0094] Implementation method: Use an IoT platform or middleware to achieve unified access, processing and forwarding of data. At the same time, use data fusion and correlation analysis technology to integrate data from different sources to form a complete data view of the entire life cycle of the device.

[0095] The device status monitoring unit monitors and analyzes the real-time status of the device based on the data provided by the Internet of Things data integration unit.

[0096] Implementation method: Real-time monitoring technology and algorithms are used to monitor and analyze the operating status and working parameters of the equipment. At the same time, early warning and alarm mechanisms are set up so that when the equipment status is abnormal, an alarm is issued in a timely manner and corresponding handling measures are taken.

[0097] The full lifecycle management unit combines RFID technology and IoT data to manage and track the entire lifecycle of equipment (from procurement, warehousing, use, maintenance to scrapping);

[0098] Implementation method: Establish a full lifecycle management database for equipment to record detailed equipment information, usage history, and maintenance records. At the same time, use data analysis technology to mine and analyze the full lifecycle data of the equipment to provide support for equipment management and decision-making.

[0099] The user interaction and display unit is responsible for providing a user-friendly interface and display method, enabling users to easily view device information, status monitoring results, and full lifecycle management data.

[0100] Implementation: Use web front-end technology or mobile application technology to build a responsive user interface; at the same time, use data visualization technology to display complex data in the form of charts, reports and other forms to improve the readability and understanding of the data.

[0101] The specific steps for using this management label are as follows:

[0102] I. System Initialization and Configuration

[0103] Module deployment and initialization

[0104] Deploy each functional module (tag information management, data collection and preprocessing, tag classification and clustering, etc.) to the server, and complete module registration and network configuration.

[0105] Initialize the tag library, relational database, and user permission system to ensure the availability of the system infrastructure.

[0106] Equipment Information Entry

[0107] Through the tag information management module, basic information of power grid equipment (such as ID, name, type, location, parameters, etc.) can be manually or in batches imported to generate unique tag codes and store them in the tag library.

[0108] II. Data Acquisition and Preprocessing

[0109] Real-time data acquisition

[0110] Deploy sensors and PLC devices to collect real-time operating status data of power grid equipment (such as voltage, current, temperature, etc.) and upload them to the system through the data acquisition unit.

[0111] Data cleaning and verification

[0112] The data preprocessing unit cleans the raw data (denoise removal, deduplication, and formatting), the verification unit verifies the integrity and accuracy of the data, and the qualified data is stored in the database or data warehouse.

[0113] III. Tag Classification and Cluster Analysis

[0114] Classification and Clustering Execution

[0115] The label classification management unit creates a classification system based on equipment characteristics (such as type and location), and the cluster analysis unit uses algorithms such as K-means and DBSCAN to cluster equipment data and generate equipment cluster labels.

[0116] Risk assessment and label optimization

[0117] The fuzzy reasoning and fault probability calculation unit combines clustering results to assess the risk level of equipment through fuzzy logic, dynamically adjust label attributes (such as "high risk" and "normal"), and store the optimized label data.

[0118] IV. Tag Storage and Relational Database Construction

[0119] Tag library management

[0120] The tag library management unit supports the creation, updating, and deletion of tags, while the basic information management unit synchronously updates basic device information (such as location changes and parameter adjustments).

[0121] Relational database construction

[0122] The relational database construction unit establishes many-to-many relationships (such as "Device A - Tag 1" and "Device B - Tag 2") based on the tag library and device information, supporting quick device queries by tag.

[0123] V. Data Analysis and Forecasting

[0124] Multi-source data fusion

[0125] The data acquisition and preprocessing unit integrates multi-source data such as sensor data and historical maintenance records, while the big data analysis unit uses techniques such as association rules and time series analysis to uncover the operating patterns of the equipment.

[0126] Fault prediction and alarm

[0127] The machine learning prediction unit trains models such as LSTM and XGBoost based on historical data to predict the probability of equipment failure; the alarm information generation and notification unit triggers alarms based on thresholds and notifies maintenance personnel via email and SMS.

[0128] VI. Visualization and Interaction

[0129] Equipment Information Visualization

[0130] The visualization module displays equipment status, classification distribution, and risk level through web / mobile applications in the form of GIS maps, dashboards, heat maps, etc., and supports filtering and drill-down analysis by tags.

[0131] User Interaction

[0132] Users can query device details, modify tag attributes, and view alarm history through the interface. The system records operation logs for auditing purposes.

[0133] VII. Contactless Management and IoT Integration

[0134] RFID data collection

[0135] The RFID data acquisition unit is equipped with readers and antennas to read RFID tag information (such as device ID and location) from devices in real time and upload it to the IoT data integration unit.

[0136] Full lifecycle management

[0137] The IoT data integration unit integrates RFID data and sensor data to form a complete lifecycle archive for the equipment; the lifecycle management unit tracks the entire process of equipment procurement, installation, operation and maintenance, and disposal, and supports tracing historical records through tags.

[0138] Status monitoring and early warning

[0139] The equipment status monitoring unit uses real-time data and algorithms such as threshold detection and trend analysis to trigger early warnings when anomalies are detected, and pushes alarm information through the user interaction and display unit.

[0140] Summary of Implementation Methods

[0141] Technology stack: Microservice architecture (Spring Cloud), relational database (MySQL) + non-relational database (MongoDB), machine learning framework (TensorFlow), IoT platform (AWS IoT), front-end framework (Vue.js).

[0142] Data flow: Device data → Acquisition → Preprocessing → Storage → Analysis → Visualization. Each module communicates decoupledly through API or message queue (Kafka).

[0143] Security mechanisms: RBAC-based access control, TLS-encrypted data transmission, and operation audit logs ensure data security and compliance.

[0144] The new type of digital management tag for physical power grid equipment is a digital management tool that integrates advanced information processing technology and Internet of Things technology. It can realize functions such as full life cycle management, visual control and efficient management of physical power grid equipment, and provide strong guarantee for the safe, stable and efficient operation of the power grid.

[0145] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0146] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0147] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0148] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0152] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A novel digital management tag system for physical power grid equipment, characterized in that, It includes modules for data acquisition and preprocessing, tag classification and clustering, tag storage and relational database, data analysis and prediction, visualization, and contactless management and IoT integration. The data acquisition and preprocessing module collects real-time status data of power grid equipment through sensors and PLC devices; The tag classification and clustering module classifies and clusters tags based on the basic information, operation information, and status data of power grid equipment, as well as the correspondence between tags and power grid equipment. The tag storage and relational database module establishes a relational database between tags and power grid equipment based on the tag library, basic tag information, and the correspondence between power grid equipment and tags. The relational database records the association between tags and power grid equipment. The data analysis and prediction module generates equipment cluster tags based on the cluster analysis results. The tag storage and relational database module associates and stores the equipment cluster tags with the equipment information of the power grid equipment, forming a many-to-many relationship.

2. The novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, It also includes a label information management module, which comprises a data storage and retrieval unit, an encoding generation and management unit, a data update and maintenance unit, and an access control and management unit; The data storage and retrieval unit is responsible for storing the basic information of the tags, including the device cluster tag, the tag's ID, name, type, and creation method. The encoding generation and management unit is responsible for generating and managing the tag IDs of the tags; The data update and maintenance unit is responsible for handling modification, addition, and deletion requests for the basic information of tags, ensuring data consistency and integrity. During concurrent updates, the data update and maintenance unit has the functions of conflict handling, backup and recovery of the basic information of tags. The recovery function refers to the fact that if the target data in the basic information becomes abnormal or incorrect due to concurrent updates during the processing of modification, addition, and deletion requests for the basic information of tags, the backup data corresponding to the target data is extracted from the backup and used to overwrite the damaged or incorrect target data. The access control and management unit is responsible for user authentication and authorization. When a user is an authorized user, the user is allowed to perform query, modification or deletion operations on the basic information of the tag. At the same time, the access control and management unit records the user's access log, providing strong support for staff auditing and troubleshooting.

3. The novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, The data acquisition and preprocessing module includes a data acquisition unit, a data preprocessing unit, a data verification and validation unit, and a data storage and management unit. The data acquisition unit uses sensors and PLC devices to acquire real-time status data of power grid equipment. The data preprocessing unit is responsible for preprocessing the status data, including noise removal, anomaly removal, duplicate removal, and formatting. The data verification and validation unit is responsible for verifying the preprocessed state data; The data storage and management unit is responsible for storing the verified status data in a database or data warehouse.

4. The novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, The tag classification and clustering module includes a data acquisition and preprocessing unit, a tag classification management unit, a cluster analysis unit, a fuzzy reasoning and fault probability calculation unit, and a data storage and management unit; The data acquisition and preprocessing unit is responsible for acquiring various target parameters of the power grid equipment in real time from various power grid equipment. Each target parameter includes basic information and operating information. The unit also performs preprocessing on each target parameter, including data cleaning, formatting, and normalization. The tag classification management unit creates a tag classification system based on the information characteristics and business needs of power grid equipment, specifies hierarchical attributes, and supports tag modification and deletion; The clustering analysis unit uses clustering algorithms to perform clustering analysis on the preprocessed power grid equipment information data, grouping similar power grid equipment information into one category to form a deep labeling system. The fuzzy reasoning and fault probability calculation unit, based on cluster analysis, uses fuzzy reasoning to enrich the label connotation and combines fault probability to assess the risk of power grid equipment. The data storage and management unit is responsible for storing the classified and clustered labels and power grid equipment information data.

5. A novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, The tag storage and relational database module includes a tag library management unit, a basic information management unit, a relational database construction unit, and a dynamic tag management unit; The tag library management unit is responsible for the creation, storage, and updating of tags; The basic information management unit is responsible for storing the basic information of the control objects of the power grid equipment. The basic information of the control objects includes equipment type, location, and parameters. The relational database construction unit constructs the association between tags and the control objects of power grid equipment based on the tag library and the basic information of power grid equipment to form a relational database. The dynamic tag management unit supports the dynamic addition, modification, and deletion of tags in the relational database.

6. A novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, The data analysis and prediction module includes a data acquisition and preprocessing unit, a big data analysis unit, a machine learning prediction unit, and an alarm information generation and notification unit. The data acquisition and preprocessing unit is responsible for collecting the operating information of the power grid equipment from multiple data sources. The big data analysis unit uses big data technology and deep mining algorithms to perform data mining on the preprocessed power grid equipment data to obtain data mining results, including hidden patterns, trends and correlations. The machine learning prediction unit uses machine learning algorithms to build a prediction model, and the prediction model processes the data mining results to predict the faults of power grid equipment. The alarm information generation and notification unit generates alarms based on the faults and thresholds of the power grid equipment, pushes the alarms to relevant personnel via email and SMS, and records the alarms so that users can query them.

7. A novel digital management tagging system for physical power grid equipment according to claim 1, characterized in that, It also includes a visualization module, which is a web platform or mobile terminal. The visualization module is used to accurately match and intelligently extract the basic information and configuration details of power grid equipment with a preset dictionary.

8. A novel digital management tag system for physical power grid equipment according to claim 1, characterized in that, It also includes a contactless management and Internet of Things (IoT) integration module, which includes an RFID data acquisition unit, an IoT data integration unit, an equipment status monitoring unit, a full lifecycle management unit, and a user interaction and display unit. The RFID data acquisition unit uses RFID technology to read power grid equipment information from RFID tags in the power grid equipment; The IoT data integration unit integrates and combines the power grid equipment information acquired by the RFID data acquisition unit with the operation information of the IoT equipment to obtain integrated data; The equipment status monitoring unit monitors and analyzes the operating status and working parameters of the power grid equipment based on the integrated data and the set early warning and alarm mechanisms. When the operating status of the power grid equipment is abnormal, it will issue an alarm in a timely manner and take corresponding measures. The full lifecycle management unit combines power grid equipment information and operational information to manage and track the entire lifecycle of power grid equipment; The user interaction and display unit is responsible for providing interactive and display interfaces, enabling users to view basic information of power grid equipment, monitoring results of operating status, and data on the entire life cycle management through these interfaces.

9. A computer device, characterized in that, include: processor; When the processor executes, it implements the low-voltage DC short-circuit fault clearing method based on power line carrier communication as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the low-voltage DC short-circuit fault clearing method based on power line carrier communication as described in any one of claims 1-8.

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