CN112487789A - Operation order scheduling logic validity verification method based on knowledge graph - Google Patents
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Abstract
The invention discloses a method for verifying the legality of scheduling logic of an operation ticket based on a knowledge graph, which comprises the steps of constructing the knowledge graph, a scheduling command and an operation term professional corpus by utilizing original data generated by a scheduling system; performing real-time voice recognition on the scheduling voice and writing the scheduling voice into scheduling characters; extracting entities and relations in the scheduling characters by using the professional corpus; and verifying the current equipment state by combining the graph matching strategy to finish the scheduling logic validity verification. The method comprises the steps of constructing a scheduling command, operating a term professional corpus and a power grid scheduling command knowledge graph, monitoring the scheduling conversation content of an on-duty dispatcher in real time through technologies such as voice recognition and the like, extracting a physical-machine relationship from the scheduling conversation content, and verifying the accuracy and the normalization of the scheduling conversation content of the dispatcher through a graph matching algorithm.
Description
Technical Field
The invention relates to the technical field of scheduling operation, in particular to a knowledge graph-based operation order scheduling logic validity verification method.
Background
At present, multiple comprehensive checks are mainly carried out on the normative and the legality of scheduling operation in the power grid scheduling process service by mainly relying on a series of administrative management means, and the method needs a duty dispatcher to verify the equipment state according to a 'five verification' requirement before filling a scheduling operation ticket, namely, the equipment state is verified with an on-site verification system, the equipment state is verified with a scheduling automation system, the equipment state is verified with a scheduling maintenance application form, the equipment state is verified with a relevant scheduling operation ticket, the equipment state is verified with a scheduling record, and then the equipment state is executed after 'three-check signature'.
At present, in order to ensure the specification and effectiveness of the dispatching operation order, a series of administrative management means are mainly adopted for multiple comprehensive customs clearance, and an on-duty dispatcher must pay attention when issuing a dispatching command and strictly comply with the specified filling and issuing of the safe working process of the national southern power grid company Limited shop. Meanwhile, before the dispatching operation order is filled, the state needs to be verified according to a 'five verification' requirement, then the dispatching operation order needs to be executed after a 'three-check' signature, but human negligence or errors exist inevitably in the process.
The current method has low execution efficiency on the scheduling service, and if a dispatcher has the condition of skip reading or even misreading in the conversation process, the dispatcher cannot timely find and correct the skip reading or skip reading, and the artificial error at the point may possibly generate potential safety hazards.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides the operation order scheduling logic validity verification method based on the knowledge graph, which can improve the execution efficiency and the safety of the current scheduling service.
In order to solve the technical problems, the invention provides the following technical scheme: constructing a knowledge graph, a scheduling command and an operation term professional corpus by using original data generated by a scheduling system; performing real-time voice recognition on the scheduling voice and writing the scheduling voice into scheduling characters; extracting entities and relations in the scheduling characters by using the professional corpus; and verifying the current equipment state by combining the graph matching strategy to finish the scheduling logic validity verification.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the verification comprises the steps of establishing a Lagrange differential equation of a target state verification model by using an energy method, wherein the Lagrange differential equation comprises the following steps:
wherein, T: the total kinetic energy of the dispatching system is,u: potential energy of a dispatching system, D: scheduling system dissipation energy, Qi: generalized coordinate qiThe corresponding generalized force.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the verification that there is a match error rate includes,
f3(X)＝f2(X)/f1(X)×100％
wherein f is1(X): frequency weighted root mean square value, f, of the state of the current device2(X): matched frequency weighted root mean square value, f3(X): matching the error rates.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the raw data comprises structured data and unstructured data set semi-structured data.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: and establishing an entity, a relation and an entity triple by utilizing the original data.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: including, the structured data is converted into the entity, the relation, and the triple of the entity through a graph mapping or a D2R conversion strategy.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the semi-structured data needs to be preprocessed to a certain extent, and is converted into knowledge and stored in the knowledge map based on entity identification and entity association.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the method further comprises the steps that the unstructured data are processed based on natural language, entity recognition and relation extraction are carried out by combining the professional corpus, the information is stored as the knowledge, and new words are searched by utilizing the entity recognition and are updated into the professional corpus.
As a preferable scheme of the operation ticket scheduling logic validity verification method based on the knowledge graph, the method comprises the following steps: the equipment is only in one state, each state is only associated with nodes which are connected together through wires, and the states among nodes cannot be converted.
The invention has the beneficial effects that: the method comprises the steps of constructing a scheduling command, operating a term professional corpus and a power grid scheduling command knowledge graph, monitoring the scheduling conversation content of an on-duty dispatcher in real time through technologies such as voice recognition and the like, extracting a physical-machine relationship from the scheduling conversation content, and verifying the accuracy and the normalization of the scheduling conversation content of the dispatcher through a graph matching algorithm.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a flowchart illustrating a method for verifying validity of a schedule logic based on a knowledge-graph according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a process of constructing a knowledge graph and a specialized corpus in the method for verifying validity of operation ticket scheduling logic based on a knowledge graph according to the first embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an entity identification and extraction process of a method for verifying validity of a schedule logic based on a knowledge-graph according to a first embodiment of the present invention;
fig. 4 is a schematic diagram of an experimental comparison output curve of the operation ticket scheduling logic validity verification method based on a knowledge graph according to the second embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 3, a method for verifying validity of an operation ticket scheduling logic based on a knowledge graph is provided as a first embodiment of the present invention, and includes:
s1: and constructing a knowledge graph, a scheduling command and an operation term professional corpus by using the original data generated by the scheduling system. Referring to fig. 2, it should be noted that the raw data includes:
structured data, unstructured data set semi-structured data;
establishing an entity, a relation and an entity triple by utilizing the original data;
converting the structured data into entities, relations and triples of the entities through a graph mapping or D2R conversion strategy;
the semi-structured data needs to be preprocessed to a certain extent, and is converted into knowledge and stored in a knowledge graph based on entity identification and entity association;
the unstructured data is processed based on natural language, entity recognition and relation extraction are carried out by combining a professional corpus, the information is stored as knowledge, and new words are searched by utilizing the entity recognition and are updated into the professional corpus.
S2: and performing real-time voice recognition on the scheduling voice and writing the scheduling voice into scheduling characters.
S3: and extracting entities and relations in the scheduling characters by using the professional corpus. Among them, it is also to be noted that:
changing the Anlongchan No. 10kv7 capacitor bank from 'hot standby' to 'cold standby';
extracting entities 'Anlongchan No. 10kv7 capacitor', 'hot standby', 'cold standby', and 'conversion' by using a professional corpus;
and verifying whether the current equipment is in a state of ' hot standby ' and can be ' converted into ' cold standby ' or not based on the graph matching strategy.
S4: and verifying the current equipment state by combining the graph matching strategy to finish the scheduling logic validity verification. It should be noted that the verification includes:
the Lagrange differential equation of the target state verification model is established by using an energy method, and the Lagrange differential equation comprises the following steps:
wherein, T: total kinetic energy of the dispatching system, U: potential energy of a dispatching system, D: scheduling system dissipation energy, Qi: generalized coordinate qiThe corresponding generalized force;
it is verified that there is a match error rate, including,
f3(X)＝f2(X)/f1(X)×100％
wherein f is1(X): frequency weighted root mean square value, f, of the current device state2(X): matched frequency weighted root mean square value, f3(X): matching the error rate;
the equipment is only in one state, each state is only associated with nodes which are connected together by wires, and states among nodes cannot be converted.
Referring to fig. 3, the primary data is subjected to data integration, entity extraction, relationship extraction, attribute extraction and knowledge extraction to complete preliminary knowledge representation, entity disambiguation, coreference resolution and entity alignment are carried out on the preliminary knowledge representation to obtain standard knowledge representation, and quality evaluation is further completed by combining knowledge reasoning and knowledge discovery to obtain a knowledge graph; and constructing a model on the basis of the known knowledge, carrying out data specification on the standard knowledge representation, and modifying the model to improve the specification so as to obtain the final knowledge map application.
Preferably, this embodiment should also be described in that the flow when the on-duty dispatcher initiates the dispatching command is as follows: for example, the call is to change the 022 disconnecting link from hot standby to cold standby, the current hot standby state of the device 022 disconnecting link is obtained through a graph matching algorithm and meets the condition, and the hot standby state node is directly associated with the cold standby state node, so that the call can be directly converted, and the secondary call meets the specification.
Preferably, the call spoken by the on-duty dispatcher due to a mistake or misreading in the dispatching process is as follows: the 022 disconnecting link is subjected to hot-standby maintenance, the current state of the 022 disconnecting link is obtained through a graph matching algorithm and is a hot standby satisfying condition, but a hot standby state node and a maintenance state node are directly not associated, so that direct conversion cannot be realized, errors or unnormality of a call are identified, and an on-duty dispatcher and a dispatched person are timely reminded, so that unnecessary loss is avoided.
Example 2
In order to better verify and explain the technical effects adopted in the method, the embodiment selects the traditional power dispatching operation order auxiliary pre-control method to perform a comparison test with the method, and compares the test results by means of scientific demonstration to verify the real effect of the method.
In order to verify that the method has higher accuracy and safety normativity compared with the traditional method, the traditional method and the method are adopted to carry out real-time measurement and comparison on the operation ticket verification of the simulation scheduling system respectively.
And (3) testing environment: operating the simulation scheduling system on a simulation platform to simulate operation and simulate the scene of different devices operating in different states, adopting historical device operation data as test samples, respectively utilizing risk quantitative value operation of a traditional method to test and obtain test result data; by adopting the method, the automatic test equipment is started, MATLB is used for realizing the simulation test of the method, and simulation data are obtained according to the experimental result; in each method, 100 groups of data are tested, error values of each group of data are obtained through calculation, comparison calculation is carried out on the error values and actual prediction error values input through simulation, and a final curve comparison graph is output.
Referring to fig. 4, a schematic diagram of a final result output curve of test comparison is shown, in which a solid line is a curve output by the present invention, and a dotted line is a curve output by a conventional method, according to the schematic diagram of fig. 4, it can be intuitively seen that the solid line and the dotted line gradually pull apart with increasing time, the dotted line always presents an unstable fluctuation trend and always has a lower safety increase than the solid line, although the solid line slightly fluctuates, the solid line basically tends to be stable and always keeps at the rising position of the dotted line, thereby, it is demonstrated that the accuracy of the method of the present invention is far greater than that of the conventional method, and the true effect of the method of the present invention is verified.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (9)
1. A method for verifying operation order scheduling logic validity based on a knowledge graph is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
constructing a knowledge graph, a scheduling command and an operation term professional corpus by using original data generated by a scheduling system;
performing real-time voice recognition on the scheduling voice and writing the scheduling voice into scheduling characters;
extracting entities and relations in the scheduling characters by using the professional corpus;
and verifying the current equipment state by combining the graph matching strategy to finish the scheduling logic validity verification.
2. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 1, wherein: the verification includes the steps of verifying the data to be stored,
the Lagrange differential equation of the target state verification model is established by using an energy method, and the Lagrange differential equation comprises the following steps:
wherein, T: total kinetic energy of the dispatching system, U: potential energy of a dispatching system, D: scheduling system dissipation energy, Qi: generalized coordinate qiThe corresponding generalized force.
3. The method for verifying the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 1 or 2, wherein: the verification that there is a match error rate includes,
f3(X)＝f2(X)/f1(X)×100％
wherein f is1(X): frequency weighted root mean square value, f, of the state of the current device2(X): matched frequency weighted root mean square value, f3(X): matching the error rates.
4. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 3, wherein: the raw data comprises structured data and unstructured data set semi-structured data.
5. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 4, wherein: and establishing an entity, a relation and an entity triple by utilizing the original data.
6. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 5, wherein: including, the structured data is converted into the entity, the relation, and the triple of the entity through a graph mapping or a D2R conversion strategy.
7. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 6, wherein: the semi-structured data needs to be preprocessed to a certain extent, and is converted into knowledge and stored in the knowledge map based on entity identification and entity association.
8. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 7, wherein: the method further comprises the steps that the unstructured data are processed based on natural language, entity recognition and relation extraction are carried out by combining the professional corpus, the information is stored as the knowledge, and new words are searched by utilizing the entity recognition and are updated into the professional corpus.
9. The method for validating the legality of the operation ticket dispatching logic based on the knowledge-graph as claimed in claim 8, wherein: the equipment is only in one state, each state is only associated with nodes which are connected together through wires, and the states among nodes cannot be converted.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113190693A (en) * | 2021-05-31 | 2021-07-30 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN113704491A (en) * | 2021-08-19 | 2021-11-26 | 中国铁道科学研究院集团有限公司通信信号研究所 | Road bureau configuration file error proofing system based on domain knowledge map |
| CN114781882A (en) * | 2022-04-27 | 2022-07-22 | 国网福建省电力有限公司 | Power grid dispatching cognitive map generation method based on combination of double-graph structure and double supervision |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447346A (en) * | 2016-08-29 | 2017-02-22 | 北京中电普华信息技术有限公司 | Method and system for construction of intelligent electric power customer service system |
| CN107767409A (en) * | 2017-09-22 | 2018-03-06 | 中国科学院西安光学精密机械研究所 | Consistent point drift registration method based on high-dimensional expression |
| CN108595449A (en) * | 2017-11-23 | 2018-09-28 | 北京科东电力控制系统有限责任公司 | The structure and application process of dispatch automated system knowledge mapping |
| CN109710926A (en) * | 2018-12-12 | 2019-05-03 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | Dispatching of power netwoks professional language semantic relation extraction method, apparatus and electronic equipment |
| CN110059356A (en) * | 2019-03-18 | 2019-07-26 | 中国电力科学研究院有限公司 | A kind of bulk power grid intelligent control system and method based on big data and artificial intelligence |
| CN110277086A (en) * | 2019-06-25 | 2019-09-24 | 中国科学院自动化研究所 | Phoneme synthesizing method, system and electronic equipment based on dispatching of power netwoks knowledge mapping |
| CN111241293A (en) * | 2019-12-28 | 2020-06-05 | 上海骥灏网络股份有限公司 | Knowledge graph algorithm constructed based on academic literature |
| CN111861250A (en) * | 2020-07-29 | 2020-10-30 | 广东电网有限责任公司电力调度控制中心 | Scheduling decision generation method and device, electronic equipment and storage medium |
| CN111860882A (en) * | 2020-06-17 | 2020-10-30 | 国网江苏省电力有限公司 | Method and device for constructing power grid dispatching fault processing knowledge graph |
| CN111897971A (en) * | 2020-07-29 | 2020-11-06 | 中国电力科学研究院有限公司 | Knowledge graph management method and system suitable for field of power grid dispatching control |
-
2020
- 2020-11-27 CN CN202011360408.1A patent/CN112487789B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447346A (en) * | 2016-08-29 | 2017-02-22 | 北京中电普华信息技术有限公司 | Method and system for construction of intelligent electric power customer service system |
| CN107767409A (en) * | 2017-09-22 | 2018-03-06 | 中国科学院西安光学精密机械研究所 | Consistent point drift registration method based on high-dimensional expression |
| CN108595449A (en) * | 2017-11-23 | 2018-09-28 | 北京科东电力控制系统有限责任公司 | The structure and application process of dispatch automated system knowledge mapping |
| CN109710926A (en) * | 2018-12-12 | 2019-05-03 | 内蒙古电力(集团)有限责任公司电力调度控制分公司 | Dispatching of power netwoks professional language semantic relation extraction method, apparatus and electronic equipment |
| CN110059356A (en) * | 2019-03-18 | 2019-07-26 | 中国电力科学研究院有限公司 | A kind of bulk power grid intelligent control system and method based on big data and artificial intelligence |
| CN110277086A (en) * | 2019-06-25 | 2019-09-24 | 中国科学院自动化研究所 | Phoneme synthesizing method, system and electronic equipment based on dispatching of power netwoks knowledge mapping |
| CN111241293A (en) * | 2019-12-28 | 2020-06-05 | 上海骥灏网络股份有限公司 | Knowledge graph algorithm constructed based on academic literature |
| CN111860882A (en) * | 2020-06-17 | 2020-10-30 | 国网江苏省电力有限公司 | Method and device for constructing power grid dispatching fault processing knowledge graph |
| CN111861250A (en) * | 2020-07-29 | 2020-10-30 | 广东电网有限责任公司电力调度控制中心 | Scheduling decision generation method and device, electronic equipment and storage medium |
| CN111897971A (en) * | 2020-07-29 | 2020-11-06 | 中国电力科学研究院有限公司 | Knowledge graph management method and system suitable for field of power grid dispatching control |
Non-Patent Citations (1)
| Title |
|---|
| 王长江: "拉格朗日方程在电路中的应用", 《四川职业技术学院学报》, pages 163 - 164 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113190693A (en) * | 2021-05-31 | 2021-07-30 | 冯凯 | Data processing method and device of energy storage system, electronic equipment and storage medium |
| CN113704491A (en) * | 2021-08-19 | 2021-11-26 | 中国铁道科学研究院集团有限公司通信信号研究所 | Road bureau configuration file error proofing system based on domain knowledge map |
| CN113704491B (en) * | 2021-08-19 | 2024-10-18 | 中国铁道科学研究院集团有限公司通信信号研究所 | Road office configuration file error proofing system based on domain knowledge graph |
| CN114781882A (en) * | 2022-04-27 | 2022-07-22 | 国网福建省电力有限公司 | Power grid dispatching cognitive map generation method based on combination of double-graph structure and double supervision |
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