Semantic-driven automatic updating method for electric power graph nodes

Abstract

The invention provides a semantic-driven electric power graph node automatic updating method, and relates to the technical field of information technology and intelligent power grid management, and the method comprises the following steps: S1, defining a formalized expression model; s2, realizing association relation extraction; s3, integrating to an automatic updating system; s4, monitoring updating of the main atlas; s5, triggering the updating of the sub-atlas; s6, updating operation is executed; s7, verifying an updating result; by defining a unified formalized expression model, the method can accurately describe the nodes in the electric power map and the association relationship thereof, and solves the problem of insufficient formalized expression in the prior art; by means of the natural language processing technology, text information in the electric power field can be analyzed and extracted, construction of a formalized expression model is further supported, and the accuracy and efficiency of information extraction are improved; and association relation extraction is optimized by utilizing a machine learning algorithm, and the association relation in the atlas is automatically identified and extracted through a training model, so that the accuracy and efficiency of association relation extraction are further improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of information technology and smart grid management technology, and particularly to a method for automatically updating power map nodes driven by semantics. Background Art

[0002] With the development of power systems, power map technology has played an increasingly important role in managing and analyzing power networks; however, there are still some key problems in the automatic update of power maps in the prior art: First, these technologies lack unified formal expressions and descriptions of association relationships, making it difficult to extract and express nodes and their association relationships, directly affecting the accuracy and operation efficiency of updates. For example, many current methods are difficult to accurately capture changes in node attributes or cannot correctly describe complex relationships between nodes; Second, the synchronization problem of power map updates is also a major challenge. When the main map changes, the sub - maps may not be able to synchronize these updates in a timely or complete manner, resulting in lag or errors in the addition or deletion of node attributes and relationships in the sub - maps. This untimely or incomplete synchronization seriously affects the real - time nature and accuracy of data, bringing unnecessary risks and troubles to related operations; In summary, the prior art mainly faces two major challenges in the automatic update of power maps: insufficient formal expression and synchronization update problems. There is an urgent need for new solutions to overcome these problems. Therefore, we propose a method for automatically updating power map nodes driven by semantics. Summary of the Invention

[0003] The purpose of the present invention is to solve the disadvantages existing in the prior art to enhance the automatic management level of power maps and ensure the real - time nature and accuracy of data.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions: A method for automatically updating power map nodes driven by semantics, the method comprising the following steps: S1. Define a formal expression model. Drawing on geographic information elements in geographic information systems and combining the characteristics of power grid services, study and define a formal expression model applicable to power maps; S2. Implement the extraction of association relationships. Based on the formal expression model, develop an algorithm to automatically extract the association relationships between power map nodes; S3. Integrate into an automatic update system. Integrate the formal expression model and the association relationship extraction algorithm into the power map automatic update system. During the update process, the system uses the model and algorithm to uniformly process and express map nodes and their association relationships; S4. Monitor the update of the main power grid map. Using the message queue technology Apache Kafka, monitor the update of the main power grid map in real time. When the main power grid map is updated, send the update information to the message queue; S5. Trigger the update of the sub - power grid map. The sub - power grid map update system subscribes to the update information in the message queue. When new update information is detected, trigger the synchronous update task of the sub - power grid map; S6. Execute the update operation. According to the update content of the main power grid map, perform corresponding update operations on the sub - power grid map; S7. Verify the update result. Use version control technology to record the update history of the sub - power grid map and conduct verification. By comparing the sub - power grid map and the main power grid map before and after the update, ensure that the update result is accurate and error - free.

[0005] The said expression model includes elements of time, location, equipment, event, status, and operation.

[0006] The said association relationships include inclusion relationships. The inclusion relationships include, but are not limited to, equipment inclusion relationships, system inclusion relationships, and function inclusion relationships; It also includes causal relationships. The causal relationships include, but are not limited to, fault causal relationships, operation causal relationships, maintenance causal relationships, and environmental causal relationships. These relationships are accurately identified and expressed through algorithms.

[0007] The said formal expression model is supported by natural language processing technology, which is used to parse and extract text information in the power field to support the construction of the formal expression model; The implementation of formal expression and the extraction of association relationships are based on graph database technology. Neo4j is selected to store and query the power grid map; Machine learning algorithms are adopted to optimize the accuracy and efficiency of association relationship extraction. Through training the model, automatically identify and extract the association relationships in the power grid map.

[0008] The corresponding update operations on the sub - power grid map include the update of node attributes, the deletion and addition of nodes and relationships. The update operations ensure that the sub - power grid map is consistent with the main power grid map.

[0009] Record the update history of the sub - power grid map through version control technology Git, trace back and verify the update result to ensure the accuracy and consistency of the data; Realize data synchronization between the main power grid map and the sub - power grid map through the data synchronization tool Debezium.

[0010] Compared with the prior art, the beneficial effects of the present invention are: 1. Improve the accuracy and efficiency of power grid map update: By defining a unified formal expression model, the present invention can accurately describe the nodes and their association relationships in the power grid map, solving the problem of insufficient formal expression in the prior art; With the help of natural language processing technology, the present invention can parse and extract text information in the power field, further supporting the construction of a formal expression model and improving the accuracy and efficiency of information extraction. The extraction of association relationships is optimized using machine learning algorithms. By training the model to automatically identify and extract the association relationships in the graph, the accuracy and efficiency of the extraction of association relationships are further improved.

[0011] 2. Realize the real-time synchronous update of the power graph: The message queue technology is used to monitor the update of the main power graph in real time, ensuring that when the main graph changes, the sub-graph can obtain the updated information in a timely manner. Data synchronization between the main graph and the sub-graph is achieved through a data synchronization tool, solving the problem of lag or error in synchronous update in the prior art and ensuring the real-time nature and accuracy of the data. Brief Description of the Drawings

[0012] Figure 1 It is a flowchart of the method for automatically updating nodes of a semantics-driven power graph provided by the present invention. Detailed Embodiments

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0014] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant content. Several embodiments of the present invention are given. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0015] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may also be a middle element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a middle element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the description of this invention are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. Embodiment

[0017] As Figure 1 shown, the present invention provides a method for automatically updating power map nodes based on semantic drive. The specific steps of this method are as follows: S1. Define a formal expression model: First, we draw on the geographic information elements in the Geographic Information System (GIS) and combine the unique characteristics of power grid operations to study and define a set of formal expression models applicable to power maps; This model covers key elements such as time, location, equipment (things), events, states (phenomena), and operations (scenarios), providing a unified and accurate expression framework for the nodes and association relationships of power maps; S2. Implement association relationship extraction: Based on the defined formal expression model, specialized algorithms are used to automatically extract the association relationships between power map nodes. These association relationships include, but are not limited to, equipment inclusion relationships, system inclusion relationships, functional inclusion relationships, as well as fault causal relationships, operation causal relationships, maintenance causal relationships, and environmental causal relationships. By applying natural language processing (NLP) techniques and machine learning algorithms, we can accurately identify and express these complex association relationships; It should be added that the inclusion relationship is usually used in power maps to represent a composition or hierarchical structure, describing the physical or logical combination between equipment and facilities. Specifically: Equipment inclusion relationship: A transformer is included in a substation, which is one of the most common inclusion relationships, indicating that the transformer is an important component of the substation; a circuit breaker is included in a switchgear: The circuit breaker is usually installed in the switchgear to control and protect the circuit; a line is included in a transmission corridor.

[0018] System inclusion relationship: A distribution system is included in a power system. The distribution system is a subset of the power system and is responsible for distributing electrical energy from the substation to the user end; an automated control system is included in a smart grid: The automated control system in the smart grid is used to monitor and control the operating status of the power grid.

[0019] Functional inclusion relationship: The protection function is included in a relay protection device. The relay protection device has multiple protection functions, such as overcurrent protection, differential protection, etc.; the monitoring function is included in a SCADA system: The SCADA system (Supervisory Control and Data Acquisition System) has the function of real-time monitoring of the operating status of the power grid.

[0020] Furthermore, causal relationships are used in the power spectrum to represent a change in one event or state caused by another event or state change, which is implemented in understanding the operation mechanism of the power system and fault troubleshooting. Specifically: Fault causal relationship: A fault causes a power outage. When a device in the power grid fails, it may cause a power outage in the area served by that device; Overload causes device damage. When a device operates in an overloaded state for a long time, it may cause device damage or failure; Operation causal relationship: An operation causes a state change. For example, operating a switch will cause a change in the connection state of the circuit; Adjusting parameters causes a performance change. For example, adjusting the tap changer of a transformer can change its output voltage, thereby affecting the voltage stability of the power grid.

[0021] Maintenance causal relationship: Maintenance leads to an extended device lifespan. Regular maintenance and servicing of a device can extend its service life; Repair leads to fault recovery. When a device fails, timely repair can restore its normal operating state.

[0022] Environmental causal relationship: Weather changes cause load changes. For example, high temperatures in summer will cause an increase in air conditioning load, thereby affecting the load level of the power grid.

[0023] S3. Integrate into the automatic update system: Next, we integrate the formal expression model and the association relationship extraction algorithm into the power spectrum automatic update system; During the update process, the system can use these models and algorithms to uniformly process and express the spectrum nodes and their association relationships, thereby realizing the automatic update and management of the power spectrum.

[0024] S4. Monitor the update of the main spectrum: We use the message queue technology Apache Kafka to monitor the update of the power main spectrum in real time. When any update occurs in the main spectrum, these update messages will be sent to the Apache Kafka message queue in a timely manner. The high availability and scalability of Apache Kafka ensure the timely transmission and accurate reception of the update messages; Furthermore, the sub-spectrum update system subscribes to the update messages in the message queue. Once new update messages are detected, the system will immediately trigger the synchronous update task of the sub-spectrum; S5. Trigger the update of the sub-spectrum: After receiving the update message of the main spectrum, the sub-spectrum update system will automatically trigger the synchronous update task. This step ensures that the sub-spectrum can reflect the latest state of the main spectrum in real time and accurately.

[0025] S6. Perform update operations: Based on the updated content of the main graph, the sub-graph update system will perform corresponding update operations on the sub-graph; these operations include updating node attributes, deleting and adding nodes and relationships, etc. By performing these operations, the sub-graph can ensure consistency with the main graph.

[0026] S7. Verify the update results: Finally, we use the version control technology Git to record the update history of the sub-graph and conduct verification; by comparing the sub-graph and the main graph before and after the update, we can ensure the accuracy of the update results; At the same time, the version control function of Git also allows us to trace back and verify any changes during the update process, further ensuring the accuracy and consistency of the data In addition, we also achieved data synchronization between the main graph and the sub-graph through the data synchronization tool Debezium; Debezium can capture data changes in the main graph in real time and synchronize them to the sub-graph or other databases / systems, thus achieving real-time data synchronization and consistency.

[0027] Workflow: When using the semantic-driven automatic update method for power graph nodes of the present invention, first, a formal expression model applicable to the power graph will be defined, and algorithms will be developed to automatically extract the association relationships between nodes. Then, these models and algorithms will be integrated into the power graph automatic update system. Next, by monitoring the update situation of the main graph and sending the update information to the message queue in a timely manner, the sub-graph update system subscribes to the update information in the message queue and triggers a synchronous update task when new information is detected. Finally, the update operation is executed and the update results are verified to ensure that the sub-graph is consistent with the main graph.

[0028] In summary, the present invention provides an efficient, accurate and real-time automatic update method for power graph nodes, which improves the automation management level of the power graph, ensures the accuracy and consistency of pulling data, and is of great significance for the stable operation and fault troubleshooting of the power system.

[0029] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A semantic-driven automatic update method for power map nodes, characterized in that, The method includes the following steps: S1. Define a formal expression model. Drawing on geographical information elements in geographic information systems and combining with the characteristics of power grid operations, research and define a formal expression model applicable to power atlases; S2. Implement association relationship extraction. Based on the formal expression model, develop an algorithm to automatically extract the association relationships between nodes in the power atlas; S3. Integrate into the automatic update system. Integrate the formal expression model and the association relationship extraction algorithm into the power atlas automatic update system. During the update process, the system uses the model and algorithm to uniformly process and express the atlas nodes and their association relationships; S4. Monitor the update of the main atlas. Using the message queue technology Apache Kafka, monitor the update status of the power main atlas in real time. When the main atlas is updated, send the update information to the message queue; S5. Trigger the update of the sub-atlas. The sub-atlas update system subscribes to the update information in the message queue. When new update information is detected, trigger the synchronous update task of the sub-atlas; S6. Execute the update operation. According to the update content of the main atlas, perform corresponding update operations on the sub-atlas; S7. Verify the update result. Use version control technology to record the update history of the sub-atlas and conduct verification. By comparing the sub-atlas and the main atlas before and after the update, ensure that the update result is accurate; 2. The semantic-driven automatic update method for power spectrum nodes according to claim 1, wherein: The expression model includes elements of time, location, equipment, event, status, and operation.

3. The semantic-driven automatic update method for power map nodes according to claim 1, characterized in that: The association relationships include inclusion relationships, and the inclusion relationships include but are not limited to equipment inclusion relationships, system inclusion relationships, and function inclusion relationships; It also includes causal relationships, and the causal relationships include but are not limited to fault causal relationships, operation causal relationships, maintenance causal relationships, and environmental causal relationships. These relationships are accurately identified and expressed through algorithms.

4. The semantic-driven power map node automatic update method according to claim 3, characterized in that: The formal expression model is supported by natural language processing technology, which is used to parse and extract text information in the power field to support the construction of the formal expression model; The implementation of formal expression and the extraction of association relationships are based on graph database technology. Neo4j is selected for storing and querying the power atlas; Machine learning algorithms are adopted to optimize the accuracy and efficiency of association relationship extraction. By training the model, automatically identify and extract the association relationships in the atlas.

5. The semantic-driven automatic update method for power spectrum nodes according to claim 1, characterized in that: The corresponding update operations on the sub-atlas include the update of node attributes, the deletion and addition of nodes and relationships. The update operations ensure that the sub-atlas is consistent with the main atlas.

6. The semantic-driven automatic update method for power spectrum map nodes according to claim 1, wherein: Record the update history of the sub-atlas through version control technology Git, trace back and verify the update result to ensure the accuracy and consistency of the data; Implement data synchronization between the main atlas and the sub-atlas through the data synchronization tool Debezium.

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