CN112418736A - Graph database-based power grid scheduling method and system - Google Patents
Graph database-based power grid scheduling method and system Download PDFInfo
- Publication number
- CN112418736A CN112418736A CN202011469089.8A CN202011469089A CN112418736A CN 112418736 A CN112418736 A CN 112418736A CN 202011469089 A CN202011469089 A CN 202011469089A CN 112418736 A CN112418736 A CN 112418736A
- Authority
- CN
- China
- Prior art keywords
- data
- power grid
- graph database
- scheduling
- graph
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a graph database-based power grid scheduling method, which comprises the following steps: acquiring electric power panoramic data from a plurality of power grid management systems; extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rule comprises new word discovery, semantic association and event relation; obtaining construction data according to the key data; constructing a graph database according to the construction data and storing the graph database; and pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database. The invention also discloses a corresponding system. By implementing the method and the system, decision support information is pushed to power grid scheduling personnel by using the graph database, so that the data query and retrieval efficiency can be improved, and the development requirement of the power industry with mass data can be met.
Description
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a graph database-based power grid dispatching method and system.
Background
The electric power equipment and the power grid operation service are core assets of a national power grid, the health operation and maintenance level of the electric power equipment is related to the safe operation level of the power grid, and the research on the comprehensive management of the quality of the electric power equipment has important significance on the safe and stable operation of the power grid and the operation and maintenance of the equipment.
With the development of the internet of things technology, all business departments related to the management of the power equipment of the national power grid company Limited establish corresponding information management systems, and the omnibearing management of purchasing, scheduling operation, overhauling, maintenance and the like is realized from different side surfaces of the power equipment. However, the conventional grid information management system uses the conventional relational database, which is difficult to meet the development requirements of the power industry.
Disclosure of Invention
In order to solve the technical problems, the invention provides a graph database-based power grid scheduling method and system so as to meet the development requirements of the power industry.
As an aspect of the present invention, there is provided a graph database-based power grid scheduling method, including:
step S101, acquiring electric power panoramic data from a plurality of power grid management systems;
step S102, extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rule comprises new word discovery, semantic association and event relation;
step S103, obtaining construction data according to the key data;
step S104, constructing a graph database according to the construction data and storing the graph database;
and step S105, pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database.
Preferably, the power panoramic data comprises historical storage SCADA measurement data, power grid topology data, power generation data, load prediction data, an overhaul plan, a scheduling rule, a scheduling plan, an operation ticket, historical fault alarms, geographic information and meteorological information.
Preferably, after the power panoramic data is acquired, the method further comprises:
the unstructured power panorama data is processed into structured data.
Preferably, obtaining the construction data according to the key data specifically includes:
dividing the key data into a training set and a test set;
selecting an LSTM network model, and performing parameter training on the LSTM by adopting the training set to obtain a target model;
and inputting the test set into the target model to obtain the constructed data.
Preferably, the graph database comprises a scheduling specification graph, a scheduling plan graph, a power grid event graph and an abnormal event graph;
pushing multi-level and multi-stage decision support information to a power grid dispatching personnel according to the graph database specifically comprises the following steps:
in the scheduling specification map, scheduling operation detailed rules and cautions of the current power grid situation are obtained, and standard disposal guidance is provided;
intelligently inquiring in the scheduling plan map, extracting and pushing key information in the accident plan suitable for the current power grid risk situation;
reasoning is carried out based on the similarity of the power grid event map and the abnormal event map, and effective key features and corresponding operation information in the historical disposal cases are extracted and pushed.
Accordingly, in another aspect of the present invention, there is also provided a graph database-based power grid dispatching system, including:
the acquisition unit is used for acquiring electric power panoramic data from a plurality of power grid management systems;
the extraction unit is used for extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rule comprises new word discovery, semantic association and event relation;
the processing unit is used for obtaining construction data according to the key data;
the construction unit is used for constructing a graph database according to the construction data and storing the graph database;
and the pushing unit is used for pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database.
Preferably, the power panoramic data comprises historical storage SCADA measurement data, power grid topology data, power generation measurement data, load prediction data, an overhaul plan, a scheduling regulation, a scheduling plan, an operation ticket, historical fault alarms, geographic information and meteorological information;
the processing unit is further configured to process unstructured power panorama data into structured data.
Preferably, the construction unit further comprises:
the dividing unit is used for dividing the key data into a training set and a test set;
the target model obtaining unit is used for selecting an LSTM network model and carrying out parameter training on the LSTM by adopting the training set to obtain a target model;
and the construction data obtaining unit is used for inputting the test set into the target model to obtain the construction data.
Preferably, the graph database comprises a scheduling specification graph, a scheduling plan graph, a power grid event graph and an abnormal event graph;
the push unit further comprises:
the guiding information obtaining unit is used for obtaining scheduling operation detailed rules and cautionary matters of the current power grid situation in the scheduling specification map and providing standard disposal guidance;
the query unit is used for intelligently querying the scheduling plan map, extracting and pushing key information in the accident plan suitable for the current power grid risk situation;
and the reasoning processing unit is used for reasoning based on the similarity of the power grid event map and the abnormal event map, and extracting and pushing effective key features and corresponding operation information in the historical treatment case.
Accordingly, in yet another aspect of the present invention, there is provided a grid dispatching system based on a graph database, which includes a processor, an input device, an output device, and a memory, wherein the processor, the input device, the output device, and the memory are connected to each other, and the memory is used for storing a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the foregoing method.
The implementation of the invention has the following beneficial effects:
the invention provides a graph database-based power grid scheduling method and system, wherein the embodiment of the invention is implemented to obtain power panoramic data from a plurality of power grid management systems; extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; obtaining construction data according to the key data; constructing a graph database according to the construction data and storing the graph database; pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database; the embodiment of the invention utilizes the graph database to push the decision support information to the power grid dispatching personnel, can improve the data query and retrieval efficiency, and meets the development requirement of the power industry with mass data.
Drawings
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 introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
FIG. 1 is a main flow diagram of one embodiment of a graph-based database grid scheduling method provided by the present invention;
FIG. 2 is a block diagram of a first embodiment of a graph-based grid dispatching system provided by the present invention;
FIG. 3 is a schematic diagram of the construction of the building block of FIG. 2;
fig. 4 is a schematic structural diagram of the pushing unit in fig. 2;
fig. 5 is a block diagram of a second embodiment of a graph database based grid dispatching system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
For a better understanding of the embodiments of the present invention, the related knowledge involved is described as follows:
different from the essence of a relational data structure based on indexes, a graph database stores data by taking 'nodes' and 'edges' as basic storage units, can provide 'non-index' correlation operation for adjacent nodes by utilizing the characteristic of physical mutual 'pointing' between the nodes, has the characteristic of real-time dynamic update of states, supports large data distributed storage and parallel computation, effectively improves the efficiency of data storage and processing, is particularly suitable for managing, processing and analyzing the application problem of interdependence relations among a large number of objects, and is a database specially processing 'mass relations'.
Referring to fig. 1, an embodiment of the present invention provides a graph database-based power grid scheduling method, including:
step S101, acquiring power panoramic data from a plurality of power grid management systems.
The electric power panoramic data comprises, but is not limited to, historical storage SCADA measurement data, power grid topology data, power generation measurement data, load prediction data, an overhaul plan, a scheduling rule, a scheduling plan, an operation ticket, historical fault alarms, geographic information, meteorological information and the like.
Step S102, processing unstructured electric power panoramic data into structured data.
And step S103, extracting the power panoramic data based on a preset data extraction rule to obtain key data.
The data extraction rules include, but are not limited to, new word discovery, semantic association, event relationship, and the like.
And step S104, obtaining construction data according to the key data.
With the rapid development of big data and machine learning technology, algorithms such as Recurrent Neural Network (RNN), long-term memory (LSTM) and mixed model clustering are gradually applied to the power industry. In this embodiment, the LSTM model is also used for data construction, and the specific process is as follows:
dividing the key data into a training set and a test set;
selecting an LSTM network model, and performing parameter training on the LSTM by adopting the training set to obtain a target model;
and inputting the test set into the target model to obtain the constructed data.
It should be noted that, in a specific application, the object model may be iterated and updated continuously, so that more prepared construction data is obtained through the object model, thereby providing a better basis for the construction of a subsequent graph database.
And step S105, constructing a graph database according to the construction data, and storing the graph database.
The constructed graph database includes, but is not limited to, a scheduling specification graph, a scheduling plan graph, a grid event graph, an abnormal event graph and the like.
And S106, pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database.
Specifically, step S106 includes:
in the scheduling specification map, scheduling operation detailed rules and cautions of the current power grid situation are obtained, and standard disposal guidance is provided;
intelligently inquiring in the scheduling plan map, extracting and pushing key information in the accident plan suitable for the current power grid risk situation;
reasoning is carried out based on the similarity of the power grid event map and the abnormal event map, and effective key features and corresponding operation information in the historical disposal cases are extracted and pushed.
According to the power grid scheduling method, the power panoramic data are acquired from the multiple power grid management systems; extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; obtaining construction data according to the key data; constructing a graph database according to the construction data and storing the graph database; pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database; namely, the embodiment of the invention utilizes the graph database to push the decision support information to the power grid dispatching personnel, so that the data query and retrieval efficiency can be improved, and the development requirement of the power industry with mass data can be met.
Based on the same inventive concept, as shown in fig. 2, a schematic structural diagram of a first embodiment of a graph database-based power grid dispatching system provided by the invention is shown. Referring to fig. 3 and 4 together, in the present embodiment, the system includes:
an obtaining unit 10, configured to obtain power panoramic data from a plurality of power grid management systems; the electric power panoramic data comprises, but is not limited to, historical storage SCADA (supervisory control and data acquisition) measurement data, power grid topological data, power generation measurement data, load prediction data, an overhaul plan, a scheduling regulation, a scheduling plan, an operation ticket, historical fault alarms, geographic information, meteorological information and the like;
the extraction unit 11 is configured to extract the power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rules include, but are not limited to, new word discovery, semantic association, event relationship, and the like;
the processing unit 12 is configured to process unstructured power panoramic data into structured data, and obtain construction data according to the key data;
the construction unit 13 is used for constructing a graph database according to the construction data and storing the graph database; wherein the graph database includes, but is not limited to, a scheduling specification graph, a scheduling plan graph, a grid event graph, an abnormal event graph, and the like
And the pushing unit 14 is used for pushing multi-level and multi-stage decision support information to a power grid dispatching personnel according to the graph database.
Further, the building unit 13 further includes:
a dividing unit 130, configured to divide the key data into a training set and a test set;
a target model obtaining unit 131, configured to select an LSTM network model, and perform parameter training on the LSTM by using the training set to obtain a target model;
a build data obtaining unit 132, configured to input the test set into the target model to obtain the build data.
Further, the pushing unit 14 further includes:
a guidance information obtaining unit 140, configured to obtain scheduling operation rules and cautions of the current power grid situation in the scheduling specification map, and provide standard treatment guidance;
the query unit 141 is configured to perform intelligent query in the scheduling plan map, and extract and push key information in the accident plan applicable to the current grid risk situation;
and the inference processing unit 142 is configured to perform inference based on the similarity between the grid event graph and the abnormal event graph, and extract and push effective key features and corresponding operation information in the historical treatment case.
Optionally, in another preferred embodiment of the present invention, as shown in fig. 5, a main structure diagram of a graph database-based power grid dispatching system is shown, and in this embodiment, the system may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected via a bus 105. The memory 104 is used for storing a computer program comprising program instructions, the processor 101 being configured for invoking the program instructions for performing the method shown in fig. 1.
It should be understood that, in the embodiment of the present invention, the Processor 101 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard or the like, and the output device 103 may include a display (LCD or the like), a speaker, or the like.
The memory 104 may include read-only memory and random access memory, and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store device type information.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiment of the present invention may execute the implementation manner described in the embodiment of the graph database-based power grid scheduling method provided in the embodiment of the present invention, and details are not described herein again.
By implementing the power grid dispatching system provided by the embodiment of the invention, decision support information is pushed to power grid dispatching personnel by using the graph database, so that the data query and retrieval efficiency can be improved, and the development requirement of the power industry with mass data can be met.
Accordingly, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, the computer program comprising program instructions that, when executed by a processor, implement: the power grid dispatching method based on the graph database.
The computer readable storage medium may be an internal storage unit of the system according to any of the foregoing embodiments, for example, a hard disk or a memory of the system. The computer readable storage medium may also be an external storage device of the system, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) Card, Flash memory Card (Flash Card), etc. provided on the system. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the system. The computer-readable storage medium is used for storing the computer program and other programs and data required by the system. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiment of the invention has the following beneficial effects:
the invention provides a graph database-based power grid scheduling method and system, which are characterized in that power panoramic data are acquired from a plurality of power grid management systems; extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; obtaining construction data according to the key data; constructing a graph database according to the construction data and storing the graph database; pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database; the embodiment of the invention utilizes the graph database to push the decision support information to the power grid dispatching personnel, can improve the data query and retrieval efficiency, and meets the development requirement of the power industry with mass data.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. A graph database-based power grid dispatching method is characterized by comprising the following steps:
step S101, acquiring electric power panoramic data from a plurality of power grid management systems;
step S102, extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rule comprises new word discovery, semantic association and event relation;
step S103, obtaining construction data according to the key data;
step S104, constructing a graph database according to the construction data and storing the graph database;
and step S15, pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database.
2. The power grid scheduling method of claim 1 wherein the power landscape data includes historical inventory SCADA survey data, grid topology data, power generation data, load forecast data, service plans, scheduling codes, scheduling plans, operating tickets, historical fault alerts, geographic information, and meteorological information.
3. The power grid scheduling method of claim 1, wherein after acquiring the power panoramic data, the method further comprises:
the unstructured power panorama data is processed into structured data.
4. The power grid scheduling method according to claim 3, wherein obtaining the construction data according to the key data specifically comprises:
dividing the key data into a training set and a test set;
selecting an LSTM network model, and performing parameter training on the LSTM by adopting the training set to obtain a target model;
and inputting the test set into the target model to obtain the constructed data.
5. The power grid scheduling method of claim 1 wherein the graph database comprises a scheduling specification graph, a scheduling plan graph, a power grid event graph, and an abnormal event graph;
pushing multi-level and multi-stage decision support information to a power grid dispatching personnel according to the graph database specifically comprises the following steps:
in the scheduling specification map, scheduling operation detailed rules and cautions of the current power grid situation are obtained, and standard disposal guidance is provided;
intelligently inquiring in the scheduling plan map, extracting and pushing key information in the accident plan suitable for the current power grid risk situation;
reasoning is carried out based on the similarity of the power grid event map and the abnormal event map, and effective key features and corresponding operation information in the historical disposal cases are extracted and pushed.
6. A graph database based power grid dispatching system, comprising:
the acquisition unit is used for acquiring electric power panoramic data from a plurality of power grid management systems;
the extraction unit is used for extracting the electric power panoramic data based on a preset data extraction rule to obtain key data; the data extraction rule comprises new word discovery, semantic association and event relation;
the processing unit is used for obtaining construction data according to the key data;
the construction unit is used for constructing a graph database according to the construction data and storing the graph database;
and the pushing unit is used for pushing multi-level and multi-stage decision support information to power grid dispatching personnel according to the graph database.
7. The power grid dispatching system of claim 6, wherein the power landscape data comprises historical inventory SCADA metrology data, grid topology data, power generation data, load forecast data, service plans, dispatching protocols, dispatching plans, operation tickets, historical fault alerts, geographic information, and meteorological information;
the processing unit is further configured to process unstructured power panorama data into structured data.
8. The grid dispatching system of claim 6, wherein the building unit further comprises:
the dividing unit is used for dividing the key data into a training set and a test set;
the target model obtaining unit is used for selecting an LSTM network model and carrying out parameter training on the LSTM by adopting the training set to obtain a target model;
and the construction data obtaining unit is used for inputting the test set into the target model to obtain the construction data.
9. The power grid scheduling system of claim 8 wherein the graph database comprises a scheduling specification graph, a scheduling plan graph, a power grid event graph, and an exception event graph;
the push unit further comprises:
the guiding information obtaining unit is used for obtaining scheduling operation detailed rules and cautionary matters of the current power grid situation in the scheduling specification map and providing standard disposal guidance;
the query unit is used for intelligently querying the scheduling plan map, extracting and pushing key information in the accident plan suitable for the current power grid risk situation;
and the reasoning processing unit is used for reasoning based on the similarity of the power grid event map and the abnormal event map, and extracting and pushing effective key features and corresponding operation information in the historical treatment case.
10. A graph database based grid dispatching system, comprising a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used for storing a computer program comprising program instructions, and wherein the processor is configured to invoke the program instructions to perform the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011469089.8A CN112418736A (en) | 2020-12-15 | 2020-12-15 | Graph database-based power grid scheduling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011469089.8A CN112418736A (en) | 2020-12-15 | 2020-12-15 | Graph database-based power grid scheduling method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112418736A true CN112418736A (en) | 2021-02-26 |
Family
ID=74776231
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011469089.8A Pending CN112418736A (en) | 2020-12-15 | 2020-12-15 | Graph database-based power grid scheduling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112418736A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113704489A (en) * | 2021-08-17 | 2021-11-26 | 深圳供电局有限公司 | Power grid dispatching knowledge graph forming method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110727741A (en) * | 2019-09-29 | 2020-01-24 | 全球能源互联网研究院有限公司 | Knowledge graph construction method and system of power system |
| 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 |
| CN111930784A (en) * | 2020-07-23 | 2020-11-13 | 南京南瑞信息通信科技有限公司 | Power grid knowledge graph construction method and system |
| CN111985653A (en) * | 2020-06-24 | 2020-11-24 | 国网江苏省电力有限公司 | Power grid fault knowledge recommendation and knowledge management system and method based on knowledge graph |
-
2020
- 2020-12-15 CN CN202011469089.8A patent/CN112418736A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110727741A (en) * | 2019-09-29 | 2020-01-24 | 全球能源互联网研究院有限公司 | Knowledge graph construction method and system of power system |
| CN111985653A (en) * | 2020-06-24 | 2020-11-24 | 国网江苏省电力有限公司 | Power grid fault knowledge recommendation and knowledge management system and method based on knowledge graph |
| CN111930784A (en) * | 2020-07-23 | 2020-11-13 | 南京南瑞信息通信科技有限公司 | Power grid knowledge graph construction method and system |
| 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 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113704489A (en) * | 2021-08-17 | 2021-11-26 | 深圳供电局有限公司 | Power grid dispatching knowledge graph forming method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sheu | Dynamic relief-demand management for emergency logistics operations under large-scale disasters | |
| Gehrke et al. | Lifted dynamic junction tree algorithm | |
| CN110895495A (en) | Human error analysis method, system, computer device and storage medium | |
| CN112085378B (en) | Resource allocation method, device, computer equipment and storage medium | |
| CN109064104A (en) | Material equipment management method, system and terminal device | |
| CN113127506A (en) | Target query statement construction method and device, storage medium and electronic device | |
| CN112418736A (en) | Graph database-based power grid scheduling method and system | |
| CN102902614B (en) | A kind of dynamic monitoring and intelligent guide method | |
| CN114496195A (en) | Medical resource scheduling method based on knowledge graph and related equipment thereof | |
| CN111324594B (en) | Data fusion method, device, equipment and storage medium for grain processing industry | |
| Zhou | Uncapacitated facility layout problem with stochastic demands | |
| Gong et al. | A holographic-based model for logistics resources integration | |
| CN109146306A (en) | A kind of enterprise management system | |
| CN115099875A (en) | Data classification method based on decision tree model and related equipment | |
| CN114090407A (en) | Interface performance early warning method based on linear regression model and related equipment thereof | |
| CN112613723A (en) | Risk alarm method, system, device and medium | |
| CN113743695A (en) | International engineering project bid quotation risk management method based on big data | |
| Teymourifar et al. | A new model for location-allocation problem based on sectorization | |
| Tan et al. | Managing manufacturing technology investments: an intelligent learning system approach | |
| CN109918353A (en) | The method and terminal device of automated information processing | |
| CN110516166B (en) | Public opinion event processing method, device, processing equipment and storage medium | |
| Li | The Application of AI-Based Technology in Computer Network Operation and Maintenance | |
| Clarfeld | Exploring Hidden Networks Yields Important Insights in Disparate Fields of Study | |
| CN113283676A (en) | Power grid data analysis method and device | |
| Klashanov | Formalisation of the methodology of the construction design documents taking into account the fuzzy logic |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |