CN107908738A - The implementation method of enterprise-level knowledge mapping search engine based on power specialty dictionary - Google Patents
The implementation method of enterprise-level knowledge mapping search engine based on power specialty dictionary Download PDFInfo
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- 230000007547 defect Effects 0.000 claims description 27
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- 238000003379 elimination reaction Methods 0.000 claims description 14
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Abstract
The implementation method of enterprise-level knowledge mapping search engine based on power specialty dictionary, it is related to electric system Enterprise search engine field, realize the search using equipment as the knowledge graph structure and knowledge mapping of core of power grid full-service domain knowledge, the isolatism of search content is solved by collection of illustrative plates, realizes the unified entrance search of the full theme numeric field data of grid equipment；The present invention is used for realization the knowledge mapping structure and knowledge mapping search engine of the full subject area of power grid, with solving the problems, such as that current power network monitoring information data amount is big, it is low to obtain effective mechanism information efficiency, realize the flattening O＆M of data assets, play the value of data assets.
Description
Technical field
The present invention relates to the search technique in electric system Enterprise search engine field, more particularly to being used for realization electric power
Enterprise-level knowledge mapping search engine implementation.
Background technology
With big data technology, the continuous development of technology of Internet of things, intellectualized technology, power grid produces hundreds of millions of daily
Data, data volume rapid expansion, the relevance missing between data, the low value-added feature of data, user want to obtain information needed,
Need to click on multiple menu, do various inquiries；User is required to be familiar with the data structure of bottom, the unified data of data deficiency at the same time
Channel is accessed, greatly hinders the discovery of data assets value.
The content of the invention
For it is above-mentioned it is existing the defects of and deficiency, the technical problems to be solved by the invention be to provide electric power enterprise level and know
Know collection of illustrative plates search engine, there is provided the unified value for accessing channel, playing data assets of data assets.
Implementation method provided by the invention using grid equipment as the enterprise-level knowledge mapping search engine of core, its is specific
Realize that flow is as follows：
1) power grid subject area models, that is, completes grid equipment and run relevant assets domain business data (defect, tour, examination
Test, state evaluation, overhaul technological transformation), dispatch scada data, metering automation data, GIS topological datas (electric connecting relation),
Weather data, power transformation (oil chromatography, partial discharge, capacitive) monitoring data, people's resource system data (power supply bureau, department, teams and groups, people
Member), financial system data；The data source is subjected to Data Integration according to type, obtains set S={ S1, S2... ..., Sn,
Wherein n belongs to positive integer, and n is definite subject area number, forms the content of knowledge mapping；SiAccording to the number in related subject domain
According to i belongs to positive integer, and i ∈ [1, n], obtains Et1∈Si, Et2∈Si... ... Etm∈Si, wherein EtFor SiThe entity E of subject area
In the data that t moment produces, m belongs to positive integer, and t belongs to positive number, and t>0；
2) power grid subject area Entity recognition, combs the incidence relation information of grid equipment, according to different themes, difference
Build example collection E={ E1,E2,…Ee, wherein e belongs to positive integer, and e ∈ [1, n],Such as S4Representative provides theme
Domain, E1 organizations entity, E2 departments entity, E3 teams and groups entity, E4 personnel's entities；
3) power grid subject area entity attribute identifies, the attribute of entity object is integrated, structure entity attributes set P
={ P1,P2,…Px, wherein x attributes positive integer, and x ∈ [1, n],Such asDuring personnel's entity, P={ P1(Identity
Card number)、P2(Post)、P3(Mailbox)、P4(Phone)、P5(Professional skill is horizontal)Etc. aggregate information；
4) power grid subject area entity-relationship recognition, builds the set of relationship R between all entity objects of power grid subject area
={ R1,R2,…Ry, wherein y attributes positive integer, and y ∈ [1, n],Such as Si represent power grid production subject area when, R1(If
Standby defect elimination relation)=(E1(Personnel),E2(Equipment entity)), the relation direction of wherein R1 is E1->E2, business information is represented as personnel
Carry out defect elimination operation to equipment；
5) power grid entity-relationship-attribute identifies, builds the attribute set Rp={ Rp of entity relationship1,Rp2,…Rpz, wherein z
Attribute positive integer, and z ∈ [1, n],And for R1(Elimination of equipment defect relation)Attribute combination Rp={ Rp1(Defect presentation),
Rp2(Rejected region), Rp3(Defect cause), Rp2(Defect processing measure), Rpz, represent the corresponding attribute information of the relation；
6) power grid full-service domain knowledge collection of illustrative plates；Based on above-mentioned 1,2,3,4, the combing of 5 steps, pass through chart database, it is real
The curing of real example, relation, attribute stores, the network knowledge collection of illustrative plates in framework power grid domain, realizes the figure of power grid full-service domain knowledge
Spectrumization manages；
7) using the Chinese word segmentation machine of IK segmenter structure search engine, IK segmenter, is divided into ik_max_word, ik_
Smart segmenter ik_max_word:Text can be most fine-grained fractionation Eq:" Kunming, power supply bureau, power supply, office " ik_
smart:The fractionation Eq of most coarseness can be：" Kunming, power supply bureau "；
8) build power specialty dictionary, comb and cure power industry specialized dictionary, aid in search engine analysis and
NLP natural language processings；
9) realize and be based on power specialty dictionary, the decision tree random forests algorithm of set IK segmenter, realizes knowledge mapping
The search matching of data, main to include NLP natural language processings, identification, the identification of relation key of example, Attribute Recognition, most
The knowledge mapping of related object is returned eventually；Such as the defect elimination information of the XX transformers belonging to the XX substations of XX power supply bureaus administration=
=》XX power supply bureaus(entity)Administration(relation)XX substations(entity)It is affiliated(relation)XX transformers(entity)Defect elimination information(relation)。
Brief description of the drawings
Fig. 1 is the implementation method flow chart using grid equipment as the enterprise-level knowledge mapping search engine of core；
Fig. 2 is electric power knowledge mapping design sketch.
Embodiment
As shown in Figure 1, Figure 2, the realization provided by the invention using grid equipment as the enterprise-level knowledge mapping search engine of core
Method, it is as follows that it implements flow：
1st, power grid subject area models, that is, completes grid equipment and run relevant assets domain business data (defect, tour, examination
Test, state evaluation, overhaul technological transformation), dispatch scada data, metering automation data, GIS topological datas (electric connecting relation),
Weather data, power transformation (oil chromatography, partial discharge, capacitive) monitoring data, people's resource system data (power supply bureau, department, teams and groups, people
Member), financial system data；The data source is subjected to Data Integration according to type, obtains set S={ S1, S2... ..., Sn,
Wherein n belongs to positive integer, and n is definite subject area number, forms the content of knowledge mapping；SiAccording to the number in related subject domain
According to i belongs to positive integer, and i ∈ [1, n], obtains Et1∈Si, Et2∈Si... ... Etm∈Si, wherein EtFor SiThe entity E of subject area
In the data that t moment produces, m belongs to positive integer, and t belongs to positive number, and t>0；
2nd, power grid subject area Entity recognition, combs the incidence relation information of grid equipment, according to different themes, difference
Build example collection E={ E1,E2,…Ee, wherein e belongs to positive integer, and e ∈ [1, n],Such as S4Representative provides theme
Domain, E1 organizations entity, E2 departments entity, E3 teams and groups entity, E4 personnel's entities；
3rd, power grid subject area entity attribute identifies, the attribute of entity object is integrated, structure entity attributes set P
={ P1,P2,…Px, wherein x attributes positive integer, and x ∈ [1, n],Such asDuring personnel's entity, P={ P1(Identity
Card number)、P2(Post)、P3(Mailbox)、P4(Phone)、P5(Professional skill is horizontal)Etc. aggregate information；
4th, power grid subject area entity-relationship recognition, builds the set of relationship R between all entity objects of power grid subject area
={ R1,R2,…Ry, wherein y attributes positive integer, and y ∈ [1, n],Such as Si represent power grid production subject area when, R1(If
Standby defect elimination relation)=(E1(Personnel),E2(Equipment entity)), the relation direction of wherein R1 is E1->E2, business information is represented as personnel
Carry out defect elimination operation to equipment；
5th, power grid entity-relationship-attribute identifies, builds the attribute set Rp={ Rp of entity relationship1,Rp2,…Rpz, wherein z
Attribute positive integer, and z ∈ [1, n],And for R1(Elimination of equipment defect relation)Attribute combination Rp={ Rp1(Defect presentation),
Rp2(Rejected region), Rp3(Defect cause), Rp2(Defect processing measure), Rpz, represent the corresponding attribute information of the relation；
6th, power grid full-service domain knowledge collection of illustrative plates；Based on above-mentioned 1,2,3,4, the combing of 5 steps, pass through chart database, it is real
The curing of real example, relation, attribute stores, the network knowledge collection of illustrative plates in framework power grid domain, realizes the figure of power grid full-service domain knowledge
Spectrumization manages；
7th, using the Chinese word segmentation machine of IK segmenter structure search engine, IK segmenter, is divided into ik_max_word, ik_
Smart segmenter ik_max_word:Text can be most fine-grained fractionation Eq:" Kunming, power supply bureau, power supply, office " ik_
smart:The fractionation Eq of most coarseness can be：" Kunming, power supply bureau "；
8th, build power specialty dictionary, comb and cure power industry specialized dictionary, aid in search engine analysis and
NLP natural language processings；
9th, realize and be based on power specialty dictionary, the decision tree random forests algorithm of set IK segmenter, realizes knowledge mapping
The search matching of data, main to include NLP natural language processings, identification, the identification of relation key of example, Attribute Recognition, most
The knowledge mapping of related object is returned eventually；Such as the defect elimination information of the XX transformers belonging to the XX substations of XX power supply bureaus administration=
=》XX power supply bureaus(entity)Administration(relation)XX substations(entity)It is affiliated(relation)XX transformers(entity)Defect elimination information(relation)。
Claims (1)
1. the implementation method of the enterprise-level knowledge mapping search engine based on power specialty dictionary, it is characterised in that specific implementation
Flow is as follows：
1) power grid subject area models, that is, completing the relevant assets domain business data of grid equipment operation includes：Defect, tour, examination
Test, state evaluation, overhaul technological transformation data；Scada data, metering automation data are dispatched, GIS topological datas are electrically connected and close
Coefficient evidence, weather data, power transformation include oil chromatography, partial discharge, the monitoring data of capacitive, and people's resource system data include power supply
Office, department, teams and groups, personnel, financial system data modeling；The data source is subjected to Data Integration according to type, obtains set S
={ S1, S2... ..., Sn, wherein n belongs to positive integer, and n is definite subject area number, forms the content of knowledge mapping；SiRoot
According to the data in related subject domain, i belongs to positive integer, and i ∈ [1, n], obtains Et1∈Si, Et2∈Si... ... Etm∈Si, wherein Et
For SiThe data that the entity E of subject area is produced in t moment, m belong to positive integer, and t belongs to positive number, and t>0；
2) power grid subject area Entity recognition, combs the incidence relation information of grid equipment, according to different themes, builds respectively
Example collection E={ E1,E2,…Ee, wherein e belongs to positive integer, and e ∈ [1, n],Such as S4Representative provides subject area, E1
Organization's entity, E2 departments entity, E3 teams and groups entity, E4 personnel's entities；
3) power grid subject area entity attribute identifies, the attribute of entity object is integrated, structure entity attributes set P=
{P1,P2,…Px, wherein x attributes positive integer, and x ∈ [1, n],Such asDuring personnel's entity, P={ P1(identity card
Number), P2(post), P3(mailbox), P4(phone), P5(professional skill horizontal) } etc. aggregate information；
4) power grid subject area entity-relationship recognition, builds the set of relationship R={ R between all entity objects of power grid subject area1,
R2,…Ry, wherein y attributes positive integer, and y ∈ [1, n],Such as Si represent power grid production subject area when, R1(equipment disappears
The relation of lacking)=(E1(personnel), E2(equipment entity)), the relation direction of wherein R1 is E1->E2, business information is represented as personnel couple
Equipment carries out defect elimination operation；
5) power grid entity-relationship-attribute identifies, builds the attribute set Rp={ Rp of entity relationship1,Rp2,…Rpz, wherein z attributes
Positive integer, and z ∈ [1, n],And for R1The attribute combination Rp={ Rp of (elimination of equipment defect relation)1(defect presentation), Rp2
(rejected region), Rp3(defect cause), Rp2(defect processing measure), Rpz, represent the corresponding attribute information of the relation；
6) power grid full-service domain knowledge collection of illustrative plates；Based on above-mentioned 1,2,3,4, the combing of 5 steps, pass through chart database, realize real
The curing of example, relation, attribute stores, the network knowledge collection of illustrative plates in framework power grid domain, realizes the collection of illustrative plates of power grid full-service domain knowledge
Management；
7) using the Chinese word segmentation machine of IK segmenter structure search engine, IK segmenter, is divided into ik_max_word, ik_smart
Segmenter ik_max_word:Text can be most fine-grained fractionation Eq:" XX, power supply bureau, power supply, office " ik_smart:It can do
The fractionation Eq of most coarseness：" XX, power supply bureau "；
8) build power specialty dictionary, comb and cure power industry specialized dictionary, aid in search engine analysis and NLP from
Right Language Processing；
9) realize and be based on power specialty dictionary, the decision tree random forests algorithm of set IK segmenter, realizes knowledge mapping data
Search matching, mainly include NLP natural language processings, identification, the identification of relation key of example, Attribute Recognition, is finally returned
Return the knowledge mapping of related object；Such as the defect elimination information of the XX transformers belonging to the XX substations of XX power supply bureaus administration==》
XX power supply bureaus(entity)Administration(relation)XX substations(entity)It is affiliated(relation)XX transformers(entity)Defect elimination information(relation)。
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271531A (en) * | 2018-11-16 | 2019-01-25 | 苏州友教习亦教育科技有限公司 | Control data corporation based on O&M knowledge mapping |
| CN109635127A (en) * | 2019-02-20 | 2019-04-16 | 云南电网有限责任公司信息中心 | A kind of power equipment portrait knowledge mapping construction method based on big data technology |
| CN109685684A (en) * | 2018-12-26 | 2019-04-26 | 武汉大学 | A kind of low-voltage network topological structure method of calibration of knowledge based map |
| CN110187678A (en) * | 2019-04-19 | 2019-08-30 | 广东省智能制造研究所 | A kind of storage of manufacturing industry process equipment information and digitlization application system |
| CN110457442A (en) * | 2019-08-09 | 2019-11-15 | 国家电网有限公司 | The knowledge mapping construction method of smart grid-oriented customer service question and answer |
| CN111026883A (en) * | 2019-12-11 | 2020-04-17 | 南方电网数字电网研究院有限公司 | Knowledge graph construction method, device, equipment and medium of power business data |
| CN111414491A (en) * | 2020-04-14 | 2020-07-14 | 广州劲源科技发展股份有限公司 | Power grid industry knowledge graph construction method, device and equipment |
| CN111737489A (en) * | 2020-06-17 | 2020-10-02 | 广联达科技股份有限公司 | Building information retrieval method, device, equipment and readable storage medium |
| CN112241424A (en) * | 2020-10-16 | 2021-01-19 | 中国民用航空华东地区空中交通管理局 | Air traffic control equipment application system and method based on knowledge graph |
| CN112307356A (en) * | 2020-10-30 | 2021-02-02 | 北京百度网讯科技有限公司 | Information searching method and device, electronic equipment and storage medium |
| CN112860872A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Self-learning-based method and system for verifying semantic compliance of power distribution network operation tickets |
| CN113407681A (en) * | 2021-08-18 | 2021-09-17 | 国网浙江省电力有限公司信息通信分公司 | Energy industry public data model construction method |
| CN113779035A (en) * | 2021-09-18 | 2021-12-10 | 广东电网有限责任公司 | Transformer substation data asset management method, system and storage medium |
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109271531A (en) * | 2018-11-16 | 2019-01-25 | 苏州友教习亦教育科技有限公司 | Control data corporation based on O&M knowledge mapping |
| CN109271531B (en) * | 2018-11-16 | 2023-04-18 | 苏州友教习亦教育科技有限公司 | Data management center based on operation and maintenance knowledge graph |
| CN109685684A (en) * | 2018-12-26 | 2019-04-26 | 武汉大学 | A kind of low-voltage network topological structure method of calibration of knowledge based map |
| CN109635127A (en) * | 2019-02-20 | 2019-04-16 | 云南电网有限责任公司信息中心 | A kind of power equipment portrait knowledge mapping construction method based on big data technology |
| CN110187678A (en) * | 2019-04-19 | 2019-08-30 | 广东省智能制造研究所 | A kind of storage of manufacturing industry process equipment information and digitlization application system |
| CN110457442B (en) * | 2019-08-09 | 2022-04-26 | 国家电网有限公司 | Intelligent power grid customer service question and answer oriented knowledge graph construction method |
| CN110457442A (en) * | 2019-08-09 | 2019-11-15 | 国家电网有限公司 | The knowledge mapping construction method of smart grid-oriented customer service question and answer |
| CN111026883A (en) * | 2019-12-11 | 2020-04-17 | 南方电网数字电网研究院有限公司 | Knowledge graph construction method, device, equipment and medium of power business data |
| CN111414491A (en) * | 2020-04-14 | 2020-07-14 | 广州劲源科技发展股份有限公司 | Power grid industry knowledge graph construction method, device and equipment |
| CN111737489A (en) * | 2020-06-17 | 2020-10-02 | 广联达科技股份有限公司 | Building information retrieval method, device, equipment and readable storage medium |
| CN112241424A (en) * | 2020-10-16 | 2021-01-19 | 中国民用航空华东地区空中交通管理局 | Air traffic control equipment application system and method based on knowledge graph |
| CN112307356A (en) * | 2020-10-30 | 2021-02-02 | 北京百度网讯科技有限公司 | Information searching method and device, electronic equipment and storage medium |
| CN112860872A (en) * | 2021-03-17 | 2021-05-28 | 广东电网有限责任公司 | Self-learning-based method and system for verifying semantic compliance of power distribution network operation tickets |
| CN112860872B (en) * | 2021-03-17 | 2024-06-28 | 广东电网有限责任公司 | Power distribution network operation ticket semantic compliance verification method and system based on self-learning |
| CN113407681A (en) * | 2021-08-18 | 2021-09-17 | 国网浙江省电力有限公司信息通信分公司 | Energy industry public data model construction method |
| CN113779035A (en) * | 2021-09-18 | 2021-12-10 | 广东电网有限责任公司 | Transformer substation data asset management method, system and storage medium |
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Application publication date: 20180413 |