CN115033707A - Power transmission equipment portrait knowledge map construction method based on big data analysis technology - Google Patents
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Abstract
The invention discloses a method for constructing a knowledge graph of an portrait of power transmission equipment based on a big data analysis technology, relates to the technical field of power grid analysis, and solves the technical problems that all power grid equipment are used as entities in the knowledge graph constructing process in the prior art, so that the constructed knowledge graph is complex and not beneficial to display; according to the method, the obtained power transmission equipment is subjected to data cleaning according to a power grid topological structure, target equipment is screened out, a main entity and an auxiliary entity are determined according to key evaluation coefficients of the target equipment, then the key entities are determined according to the connection relation between the power transmission equipment, a target knowledge graph is constructed and generated, and the usability is improved under the condition that the target knowledge graph is ensured to be perfect; the method screens the power transmission equipment to obtain the main entity and the auxiliary entity, generates the key entity according to the connection relationship between the main entity and the auxiliary entity, and establishes the target knowledge graph according to the connection relationship between the power transmission equipment and the key entities, so that the accuracy and the simplicity of the target knowledge graph can be ensured.
Description
Technical Field
The invention belongs to the field of power grid analysis, relates to a power transmission equipment knowledge map construction technology based on a big data analysis technology, and particularly relates to a power transmission equipment portrait knowledge map construction method based on the big data analysis technology.
Background
The power network is large and complex in structure, and the query operation speed and the performance of the traditional database are very slow and poor. The knowledge graph technology can obviously improve the retrieval effectiveness, so that the retrieval result is more comprehensive and accurate.
The prior art (patent invention with publication number CN 111737483A) discloses a method for constructing a big data knowledge graph of a smart power grid, which is used for cleaning power grid equipment information based on a historical topology database, and establishing a topology relation knowledge graph by combining topology search analysis to realize efficient analysis of panoramic data. In the prior art, all power grid equipment are used as entities in the process of constructing the knowledge graph, so that the constructed knowledge graph is complex and is not beneficial to display; therefore, a power transmission equipment portrait knowledge map construction method based on a big data analysis technology is needed.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides a power transmission equipment portrait knowledge map construction method based on a big data analysis technology, which is used for solving the technical problems that in the process of constructing the knowledge map, all power grid equipment are used as entities in the prior art, so that the constructed knowledge map is complex and is not beneficial to display.
According to the method, the obtained power transmission equipment is subjected to data cleaning according to the power grid topological structure, the target equipment is screened out, the main entity and the auxiliary entity are determined according to the key evaluation coefficient of each target equipment, the key entity is determined according to the connection relation between each power transmission equipment, the target knowledge graph is finally constructed and generated, and the usability is improved under the condition that the target knowledge graph is ensured to be perfect.
In order to achieve the above object, a first aspect of the present invention provides a power transmission equipment portrait knowledge graph construction method based on big data analysis technology, including:
acquiring power transmission equipment and a connection relation between the power transmission equipment;
acquiring a power grid topological structure, and performing data cleaning on the power transmission equipment according to the power grid topological structure to acquire target equipment;
obtaining a key evaluation coefficient of the target equipment through historical question-answering data or equipment evaluation data, and determining a main entity and an auxiliary entity according to the key evaluation coefficient;
and integrating the auxiliary entity and the main entity through the connection relation between the power transmission equipment to generate a key entity, and establishing a target knowledge graph corresponding to the key entity by combining topology search analysis.
Preferably, the power transmission equipment and the connection relationship between the power transmission equipment are acquired through Neo4 j; the connection relation between the power transmission equipment comprises electrical connection and communication connection, and the power transmission equipment comprises a switch, a transformer, a circuit breaker, a lightning arrester and a wire cable.
Preferably, the data cleaning of the power transmission equipment according to the power grid topology structure to obtain the target equipment includes:
acquiring the existing power grid topological structure;
and cleaning the data of the power transmission equipment through the power grid topological structure, and screening the cleaned data by combining with the set keywords to obtain the target equipment.
Preferably, the obtaining the key evaluation coefficient corresponding to the target device according to the historical question and answer data includes:
acquiring the historical question and answer data corresponding to the power transmission network;
obtaining the attention degree of the target equipment and the proportion coefficient of the target equipment in the power transmission network through the historical question-answer data;
and acquiring the key evaluation coefficient corresponding to the target equipment according to the attention degree and the proportion coefficient.
Preferably, the obtaining the key evaluation coefficient corresponding to the target device according to the device evaluation data includes:
acquiring historical data corresponding to the target equipment;
extracting the cycle abnormal times of the target equipment and the proportion coefficient of the target equipment in the power transmission network through the historical data;
and acquiring the key evaluation coefficient corresponding to the target equipment according to the cycle abnormal times and the proportion coefficient.
Preferably, the determining the connection relationship between the key evaluation coefficient and the power transmission equipment to the primary entity and the secondary entity includes:
extracting the key evaluation coefficient corresponding to the target equipment;
when the key evaluation coefficient is larger than an evaluation coefficient threshold value, marking the corresponding target equipment as the main entity; otherwise, marking the corresponding target device as the auxiliary entity.
Preferably, the integrating the main entity and the subordinate entity in combination with the connection relationship between the power transmission devices includes:
acquiring the auxiliary entity connected with the main entity according to the distance or the district, and integrating the auxiliary entity with the corresponding main entity to generate the key entity; the key entity comprises a main entity and a plurality of auxiliary entities connected with the main entity.
Preferably, the establishing of the target knowledge graph corresponding to the key entity through the topology searching analysis includes:
optionally selecting one of the key entities as a target entity;
traversing the key entities adjacent to the target entity, and then traversing by taking the adjacent key entities as the target entity;
and extracting the incidence relation between the target entity and the adjacent key entity according to a traversal process, and further establishing the target knowledge graph.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the obtained power transmission equipment is subjected to data cleaning according to the power grid topological structure, the target equipment is screened out, the main entity and the auxiliary entity are determined according to the key evaluation coefficient of each target equipment, the key entity is determined according to the connection relation between each power transmission equipment, the target knowledge graph is finally constructed and generated, and the usability is improved under the condition that the target knowledge graph is ensured to be perfect.
2. According to the invention, the power transmission equipment is screened according to the importance degree to obtain the main entity and the auxiliary entity, the key entity is generated according to the connection relationship between the main entity and the auxiliary entity, and the target knowledge graph is established according to the connection relationship between the power transmission equipment and a plurality of key entities, so that the accuracy and the simplicity of the target knowledge graph can be ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the working steps of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the prior art, all power grid equipment are used as entities in the process of establishing the knowledge graph, so that the established knowledge graph is complex and the most common parts cannot be displayed.
According to the method, after the target equipment is obtained, the main entity and the auxiliary entity are determined according to the key evaluation coefficients of the target equipment, the key entity is determined according to the connection relation between the power transmission equipment, and the target knowledge graph is finally constructed and generated, so that the usability is improved under the condition that the target knowledge graph is ensured to be perfect.
Referring to fig. 1, an embodiment of the first aspect of the present application provides a power transmission equipment portrait knowledge graph construction method based on a big data analysis technology, including:
acquiring a connection relation between power transmission equipment and the power transmission equipment;
acquiring a power grid topological structure, and performing data cleaning on power transmission equipment according to the power grid topological structure to acquire target equipment;
obtaining a key evaluation coefficient of the target equipment through historical question-answer data or equipment evaluation data, and determining a main entity and an auxiliary entity according to the key evaluation coefficient;
and integrating the auxiliary entity and the main entity through the connection relation between the power transmission equipment to generate a key entity, and establishing a target knowledge graph corresponding to the key entity by combining topology search analysis.
According to the method, after the connection relation between the power transmission equipment and each power transmission equipment is obtained, the power transmission equipment is cleaned according to the power grid topology equipment, the target equipment is screened out, then the target equipment is divided into a main entity and an auxiliary entity according to the importance of the target equipment, the main entity and the adjacent auxiliary entities are integrated into a key entity, and a target knowledge graph is established based on a plurality of key entities.
In the application, a connection relation between power transmission equipment and the power transmission equipment is obtained through Neo4 j; the connection relation among the power transmission equipment comprises electrical connection, communication connection and the like, and the power transmission equipment comprises a switch, a transformer, a reactor, a breaker, a mutual inductor, an insulator, a lightning arrester, a direct-current power transmission converter valve, a wire and a cable and the like.
According to the power transmission equipment data cleaning method based on the power grid topological structure, the power transmission equipment is subjected to data cleaning, and the target equipment is obtained, and the method comprises the following steps:
acquiring an existing power grid topological structure;
and cleaning the data of the power transmission equipment through the power grid topological structure, and screening the cleaned data by combining with the set keywords to obtain the target equipment.
After the data of the power transmission equipment is cleaned according to the power grid topological structure, the target equipment needs to be screened from the power transmission equipment, and the screening can be performed according to the set keywords. The transmission equipment is screened, durable equipment which is not prone to faults is screened, and the remaining equipment is used as target equipment, so that the construction difficulty of a target knowledge graph can be reduced. It should be noted that, screening out some power transmission devices does not directly delete corresponding data, but uses the data as auxiliary data, and the auxiliary data can be associated with adjacent target devices so as to query and invoke.
In an optional embodiment, obtaining the key evaluation coefficient corresponding to the target device according to the historical question-answer data includes:
acquiring historical question and answer data corresponding to the power transmission network;
obtaining the attention degree of target equipment and the proportion coefficient of the target equipment in the power transmission network through historical question and answer data;
and obtaining a key evaluation coefficient of the corresponding target equipment according to the attention and the proportion coefficient.
Each target device is evaluated and the results are then evaluated to determine which target devices may serve as entities for constructing the knowledge graph, as well as being suitable as primary entities or being suitable as secondary entities. It can be seen that the role of the key evaluation coefficient is very important. According to the scheme, the importance of the target equipment is evaluated through historical question answering data, namely the query records of the staff on the digital display equipment, and then the corresponding correlation evaluation coefficient is obtained.
The attention degree of the target equipment refers to the total times of the target equipment in historical question answering data, and the proportion coefficient refers to the proportion of the type of the target equipment in the power transmission network. Obtaining a key evaluation coefficient of the corresponding target device according to the attention and the proportion coefficient, wherein the key evaluation coefficient comprises the following steps:
marking the attention degree and the proportion coefficient as GZ and ZX respectively;
acquiring a key evaluation coefficient corresponding to the target equipment according to a formula GPX (general formula of the target equipment), wherein the formula GPX is alpha 1 multiplied by GZ + alpha 2 multiplied by ZX; wherein α 1 and α 2 are both scaling factors greater than 0.
In another optional embodiment, obtaining the key evaluation coefficient corresponding to the target device according to the device evaluation data includes:
acquiring historical data corresponding to target equipment;
extracting the cycle abnormal times of the target equipment and the proportion coefficient of the target equipment in the power transmission network through historical data;
and acquiring a key evaluation coefficient of the corresponding target equipment according to the cycle abnormal times and the proportion coefficient.
In this embodiment, the number of abnormal times of the target device in a set period is determined by combining the historical data corresponding to the target device, and the set period includes one month, one year, and the like. Then, combining the cycle abnormal times and the proportion coefficient to obtain a key evaluation coefficient of the corresponding target device, wherein the key evaluation coefficient comprises the following steps:
respectively marking the cycle abnormal times and the proportion coefficient as ZYC and ZX;
acquiring a key evaluation coefficient corresponding to the target equipment according to a formula GPX (β 1 × ZYC + β 2 × ZX); wherein, the beta 1 and the beta 2 are both proportionality coefficients larger than 0.
The two technical schemes for obtaining the key evaluation coefficient corresponding to the target equipment can be used for evaluating the importance of the target equipment in the power transmission network, and a data base is laid for establishing a target knowledge graph. In other preferred embodiments, the key evaluation coefficient can be obtained by other data capable of representing the importance of the target equipment in the power transmission network.
In this application, determining the connection relationship between the key evaluation coefficient and the power transmission device as the main entity and the subordinate entity includes:
extracting a key evaluation coefficient corresponding to the target equipment;
when the key evaluation coefficient is larger than the evaluation coefficient threshold value, marking the corresponding target equipment as a main entity; otherwise, the corresponding target device is marked as a secondary entity.
And dividing the target equipment according to the key evaluation coefficient, specifically marking the target equipment of which the associated evaluation coefficient is greater than the evaluation coefficient threshold as a main entity, and marking the rest target equipment except the main entity as auxiliary entities.
The main entity and the auxiliary entity are both target equipment and power transmission equipment, and are screened out layer by layer under different conditions. The main entity and the auxiliary entity have inconsistent importance degrees in the power transmission network, and other attributes are consistent.
In this application, combine the relation of connection between the transmission equipment to integrate main entity and auxiliary entity, include:
acquiring an auxiliary entity connected with the main entity according to the distance or the jurisdiction, and integrating the auxiliary entity with the corresponding main entity to generate a key entity; the key entity comprises a main entity and a plurality of auxiliary entities connected with the entity.
And integrating the main entity and the auxiliary entity, and only using the main entity as a construction entity of the knowledge graph. The integration can be carried out according to the distance, for example, a main entity is selected optionally, and an auxiliary entity which is close to the main entity and has a connection relationship with the main entity is integrated with the main entity to form a key entity; and can be integrated according to the jurisdiction, for example, a main entity is selected optionally, and the auxiliary entity under the jurisdiction of the main entity is integrated with the main entity to form a key entity.
In general, according to the set rules, a main entity is selected for each auxiliary entity and is integrated with the main entity. It is understood that the key entity essentially includes a main entity and several subordinate entities, and the connection relationship between the main entity and several subordinate entities.
In the present application, establishing a target knowledge graph corresponding to a key entity through topology search analysis includes:
optionally selecting one key entity as a target entity;
traversing key entities adjacent to the target entity, and then traversing by taking the adjacent key entities as the target entity;
and extracting the incidence relation between the target entity and the adjacent key entity according to the traversal process, and further establishing the target knowledge graph.
And optionally selecting one key entity, constructing a target knowledge graph according to the connection relationship between the main entity and other main entities or between the main entities in the key entity, displaying the target knowledge graph by using the key entity, and hiding the connection relationship between the main entity and the auxiliary entities in the key entity.
It should be noted that, when the target knowledge graph is established according to the key entities, the connection relationship between the two key entities includes the connection relationship between the two main entities, and also includes the connection relationships between the main entities and the auxiliary entities, and between the auxiliary entities and the auxiliary entities.
When the target knowledge graph is displayed, only the key entities and the connection relation between the key entities can be seen, and the key entities are screened according to the knowledge, so that the important or easily-failed power transmission equipment in the power transmission network is relatively important, and the target knowledge graph in the state is concise, and the data integrity can be ensured.
When the worker queries not the main entity but the auxiliary entity, the corresponding associated entity is also positioned, and the main entity or the auxiliary entity having a connection relation with the auxiliary entity is extracted, so that a perfect query function and an accurate query result can be provided for the worker.
The working principle of the invention is as follows:
acquiring a connection relation between power transmission equipment and the power transmission equipment; and acquiring a power grid topological structure, and performing data cleaning on the power transmission equipment according to the power grid topological structure to acquire target equipment.
And obtaining a key evaluation coefficient of the target equipment through historical question-answer data or equipment evaluation data, and determining the main entity and the auxiliary entity according to the key evaluation coefficient.
And integrating the auxiliary entity and the main entity through the connection relation between the power transmission equipment to generate a key entity, and establishing a target knowledge graph corresponding to the key entity by combining topology search analysis.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. A power transmission equipment portrait knowledge map construction method based on big data analysis technology is characterized by comprising the following steps:
acquiring power transmission equipment and a connection relation between the power transmission equipment;
acquiring a power grid topological structure, and performing data cleaning on the power transmission equipment according to the power grid topological structure to acquire target equipment;
obtaining a key evaluation coefficient of the target equipment through historical question-answering data or equipment evaluation data, and determining a main entity and an auxiliary entity according to the key evaluation coefficient;
and integrating the auxiliary entity and the main entity through the connection relation between the power transmission equipment to generate a key entity, and establishing a target knowledge graph corresponding to the key entity by combining topology search analysis.
2. The big data analysis technology-based power transmission equipment portrait knowledge map construction method according to claim 1, wherein a connection relationship between a power transmission equipment and the power transmission equipment is obtained through Neo4 j; the connection relation between the power transmission equipment comprises electrical connection and communication connection, and the power transmission equipment comprises a switch, a transformer, a circuit breaker, a lightning arrester and a wire cable.
3. The big data analysis technology-based power transmission equipment portrait knowledge map construction method according to claim 1, wherein the step of performing data cleaning on the power transmission equipment according to the power grid topology structure to obtain the target equipment comprises the following steps:
acquiring the existing power grid topological structure;
and cleaning the data of the power transmission equipment through the power grid topological structure, and screening the cleaned data by combining with the set keywords to obtain the target equipment.
4. The method for constructing the portrait knowledge map of power transmission equipment based on big data analysis technology as claimed in claim 1, wherein obtaining the key evaluation coefficient corresponding to the target equipment according to the historical question-answer data comprises:
acquiring the historical question and answer data corresponding to the power transmission network;
obtaining the attention degree of the target equipment and the proportion coefficient of the target equipment in the power transmission network through the historical question-answer data;
and acquiring the key evaluation coefficient corresponding to the target equipment according to the attention degree and the proportion coefficient.
5. The big data analysis technology-based power transmission equipment portrait knowledge map construction method according to claim 1, wherein obtaining the key evaluation coefficient corresponding to the target equipment according to the equipment evaluation data comprises:
acquiring historical data corresponding to the target equipment;
extracting the cycle abnormal times of the target equipment and the proportion coefficient of the target equipment in the power transmission network through the historical data;
and acquiring the key evaluation coefficient corresponding to the target equipment according to the cycle abnormal times and the proportion coefficient.
6. The big data analysis technology-based power transmission equipment representation knowledge graph construction method according to claim 4 or 5, wherein determining the connection relation between the key evaluation coefficient and the power transmission equipment to be the main entity and the auxiliary entity comprises:
extracting the key evaluation coefficient corresponding to the target equipment;
when the key evaluation coefficient is larger than an evaluation coefficient threshold value, marking the corresponding target equipment as the main entity; otherwise, marking the corresponding target equipment as the auxiliary entity.
7. The big data analysis technology-based power transmission equipment portrait knowledge map construction method according to claim 6, wherein the integration of the main entity and the auxiliary entity in combination with the connection relationship between the power transmission equipment includes:
acquiring the auxiliary entity connected with the main entity according to the distance or the district, and integrating the auxiliary entity with the corresponding main entity to generate the key entity; the key entity comprises a main entity and a plurality of auxiliary entities connected with the main entity.
8. The power transmission equipment portrait knowledge graph construction method based on big data analysis technology as claimed in claim 7, wherein the establishing of the target knowledge graph corresponding to the key entity through the topology search analysis comprises:
optionally selecting one of the key entities as a target entity;
traversing the key entities adjacent to the target entity, and then traversing by taking the adjacent key entities as the target entity;
and extracting the incidence relation between the target entity and the adjacent key entity according to the traversal process, and further establishing the target knowledge graph.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115271277A (en) * | 2022-10-08 | 2022-11-01 | 中国电力科学研究院有限公司 | Power equipment portrait construction method and system, computer equipment and storage medium |
| CN115577122A (en) * | 2022-11-09 | 2023-01-06 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power distribution network power failure information knowledge graph |
| CN115935608A (en) * | 2022-11-10 | 2023-04-07 | 北京能科瑞元数字技术有限公司 | Equipment preassembling and assembling method based on model |
-
2022
- 2022-05-18 CN CN202210547649.XA patent/CN115033707A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115271277A (en) * | 2022-10-08 | 2022-11-01 | 中国电力科学研究院有限公司 | Power equipment portrait construction method and system, computer equipment and storage medium |
| CN115577122A (en) * | 2022-11-09 | 2023-01-06 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power distribution network power failure information knowledge graph |
| CN115577122B (en) * | 2022-11-09 | 2024-04-19 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power outage information knowledge graph of power distribution network |
| CN115935608A (en) * | 2022-11-10 | 2023-04-07 | 北京能科瑞元数字技术有限公司 | Equipment preassembling and assembling method based on model |
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