CN114548800A - Future-state power grid maintenance risk identification method and device based on power grid knowledge graph - Google Patents
Future-state power grid maintenance risk identification method and device based on power grid knowledge graph Download PDFInfo
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Abstract
The invention relates to a future state power grid maintenance risk identification method and device based on a power grid knowledge graph, wherein the method comprises the steps of constructing a power grid risk quantitative index evaluation model; training the preset power grid knowledge graph to associate power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph; and inputting the equipment information for acquiring the power failure requirement into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result and generate a risk early warning notice. The method is based on the existing blackout maintenance knowledge graph, stores the knowledge graph into the power grid safety risk quantitative evaluation standard, constructs a knowledge system for identifying the future state risk of the knowledge graph in the power grid blackout maintenance, and adopts a visualization technology to describe the excavation, analysis, construction and drawing of the knowledge graph on blackout maintenance risk information, so that a risk early warning notice result is output.
Description
Technical Field
The invention belongs to the technical field of power markets, and particularly relates to a future state power grid maintenance risk identification method and device based on a power grid knowledge graph.
Background
With the continuous and deep industrialization transformation and upgrading and electric power market reformation in the energy and electric power field of China, a large amount of new energy and new loads are accessed. The scale of power grid capital construction and production, major repair and technical improvement and line stalk migration is expanded unprecedentedly, and the form and the operation characteristic of a power system are increasingly complicated. The power grid evolves to a novel network with random source, load, storage, people and other factors and uncertainty in real time. With the change of the structure of the power grid, great challenges are faced to scheduling operation modes, power grid bearing capacity, risk bearing capacity and the like, the traditional method cannot meet the diversity requirement of power grid stability maintenance, and the artificial intelligence-based power grid maintenance risk identification becomes one of important means for predicting the future state of the power grid.
How to introduce the power grid risk identification method based on artificial intelligence in the electric power maintenance planning work realizes the effective control of various risk factors, and has theoretical significance and practical value for reducing the accident rate and improving the power supply reliability. The power grid risks caused by power failure maintenance include the following: (1) the electrical connection between the substation nodes and the grid system can be weakened through the overlapping maintenance; (2) meanwhile, the maintenance can cause the risk that the transformer substation is subjected to voltage loss and becomes an electrical island; (3) the loss of voltage of an electrical island and a transformer substation can cause the risk of regional load loss; (4) arranging the power grid operation risk caused by the N-1 fault set during maintenance; (5) and (4) overload risks of single-transformer and single-bus loads caused by load transfer caused by the N-1 fault.
In summary, most of the existing risk identification methods are determined by artificial experience in combination with a network topology. However, the subjective influence factors are more in proportion after being judged by manual experience, when a large amount of power failure needs are required to be arranged, the manual identification efficiency obviously cannot adapt to increasingly complex power grid forms and operation characteristics, and loopholes exist in the aspects of risk identification accuracy, power grid operation rationality and the like.
Disclosure of Invention
In view of the above, the present invention provides a future state power grid maintenance risk identification method and device based on a power grid knowledge graph, so as to solve the problem of vulnerability in the aspects of risk identification accuracy and power grid operation rationality in the prior art.
In order to realize the purpose, the invention adopts the following technical scheme: a future state power grid maintenance risk identification method based on a power grid knowledge graph comprises the following steps:
constructing a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value;
bringing power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, training the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph;
equipment information for acquiring power failure requirements is input into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
and generating a risk early warning notice according to the risk identification result.
Further, the power grid overhaul risk identification logic data includes:
the power grid risk hazard severity quantitative index, the social influence factor quantitative index, the loss load or user property quantitative index, the equipment type factor and the historical statistical factor.
Further, the training of the preset power grid knowledge graph includes:
knowledge modeling, data extraction, knowledge extraction, map construction, storage optimization, knowledge reasoning and scene application.
Further, the equipment information that acquires the power outage demand is input into the power grid maintenance risk identification model of the power grid knowledge base map, and a risk identification result is obtained, including:
inputting equipment information of power failure requirements to a power grid maintenance risk identification model of the power grid knowledge graph;
the power grid maintenance risk identification model of the power grid knowledge graph carries out power grid topological structure analysis on the equipment information and then carries out risk identification on the power failure demand;
and obtaining a risk identification result.
Further, the said power outage demand risk identification includes:
counting power failure users, identifying overlapped maintenance risks, identifying electrical island risks, identifying transformer substation voltage loss risks, identifying load transfer overload risks and identifying power grid risk point analysis risks.
Further, the generating a risk early warning notice according to the risk identification result includes:
and inputting the risk identification result into a list.
Further, the risk pre-warning notice comprises:
risk name, risk number, risk level, risk value, accident event level, risk start time, risk end time, and recommended action.
The embodiment of the application provides a future attitude electric wire netting overhauls risk identification device based on electric wire netting knowledge map, includes:
the first construction module is used for constructing a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value;
the second construction module is used for bringing power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, training the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph;
the calculation module is used for acquiring equipment information of power failure requirements and inputting the equipment information into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
and the generating module is used for generating a risk early warning notice according to the risk identification result.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the invention provides a future state power grid maintenance risk identification method and device based on a power grid knowledge graph. The method comprises the steps of storing a knowledge graph for power grid safety risk quantitative evaluation standard based on a ground dispatching power outage overhaul knowledge graph, constructing a knowledge system for identifying future state risks of the knowledge graph in power grid power outage overhaul, and describing excavation, analysis, construction and drawing of the knowledge graph on power outage overhaul risk information by adopting a visualization technology so as to output a risk early warning notice result.
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 described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the steps of a future state power grid overhaul risk identification method based on a power grid knowledge graph according to the invention;
FIG. 2 is a schematic flow chart of a future state power grid overhaul risk identification method based on a power grid knowledge graph according to the invention;
fig. 3 is a schematic structural diagram of the future state power grid overhaul risk identification device based on the power grid knowledge graph.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It should be apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
A specific future state grid overhaul risk identification method and device based on a grid knowledge graph provided in the embodiments of the present application are described below with reference to the accompanying drawings.
As shown in fig. 1, the method for identifying future state power grid overhaul risk based on a power grid knowledge graph provided in the embodiment of the present application includes:
s101, constructing a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value;
according to "quantitative evaluation standard of grid security risk", a grid risk value is max { (risk hazard value) × (risk probability value) }, wherein: risk value (damage severity score) x (social impact factor) x (loss load or user nature factor), risk probability value (equipment type factor) x (failure category factor) x (historical data statistics factor). According to the magnitude of the risk value of the power grid, the risk of the power grid is divided into six grades, namely grade I risk (red: the risk value is more than or equal to 1500), grade II risk (orange: the risk value is more than or equal to 800 and less than 1500), grade III risk (yellow: 120: the risk value is more than or equal to 800), grade IV risk (blue: 20: the risk value is more than or equal to 20 and less than 120), grade V risk (white: 5: the risk value is more than or equal to 20) and grade VI risk (2: the risk value is less than 5).
S102, bringing power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, training the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph;
preferably, the power grid overhaul risk identification logic data includes:
the power grid risk hazard severity quantitative index, the social influence factor quantitative index, the loss load or user property quantitative index, the equipment type factor and the historical statistical factor.
The utility model discloses a topological structure and data relation based on current electric wire netting knowledge-graph will construct electric wire netting maintenance risk identification rule data below and incorporate into the database:
and (4) quantifying indexes of the severity of the risk hazard of the power grid. According to the possible threat of the risk to the power grid safety and the degree of load loss, the severity of the damage is divided into twelve grades, and each grade of the damage corresponds to each grade of the electric power safety accident and event grade specified in the relevant specification.
And quantifying indexes of social influence factors. The quantitative indexes of the social influence factors are classified by the power supply guarantee period and are divided into the following steps: general period (1), special period guarantee power supply (1.2), secondary guarantee power supply (1.4), primary guarantee power supply (1.6) and special guarantee power supply (2).
Loss load or user properties quantify indicators. And on the basis of the Baidu map, bringing the area to which the power grid equipment belongs and the data influencing the user information into a power grid risk quantitative index evaluation model. The loss load or user property quantization indexes are divided into: county level, suburb level load (1.2), urban load (1.5), second level important user (2.1), and important user (2.3), special level user (2.5).
Device type factor: including the electrical primary equipment type factor, as shown in table 1.
The communication device type factor as shown in table 2.
Automation device type factor as shown in table 3.
Failure category factor: the fault classification factors of elements such as primary equipment, protection, stability control and the like are selected according to the requirements of safety and stability guide rules of power systems. And secondly, evaluating the grid reference risk by considering first-stage, second-stage and third-stage faults. And thirdly, evaluating the risk based on the problem, considering the first-level fault and the second-level fault specified in the safety and stability guide rule of the power system and the unconventional fault with the probability score not lower than 0.1.
The specific grades are shown in tables 4, 5, 6 and 7.
TABLE 4 Fault Category factor
| Type (B) | First order fault | Second order fault | Third order failure |
| Score value | 0.8～1.2 | 0.1～0.6 | 0～0.2 |
TABLE 5 Primary, protection, stability control element failure categories and values
TABLE 6 communication device failure class factor
| Type (B) | Fault of the first kind | Type II fault | Type three fault | Type four fault |
| Score value | 1～0.8 | 0.8～0.6 | 0.6～0.2 | 0～0.2 |
TABLE 7 Automation equipment failure class factor
TABLE 8 historical statistical factors
Historical statistical factors: and (4) a historical data statistical factor of 1+ the number of times of faults of the same type of equipment in the last year/number of the same type of equipment. Firstly, only selecting more serious yellow, orange and red early warning grades according to new-edition meteorological disaster early warning signals; second, typhoon: taking 1-2 yellow early warning, 2-3 orange early warning and 3-4 red early warning; ③ thunderstorm and strong wind: taking 1-1.2 parts of yellow early warning, 1.2-1.5 parts of orange early warning and 1.5-2 parts of red early warning; fourthly, forest fire danger: 1-1.2 orange early warning and 1-1.5 red early warning; high temperature: 1.1 is taken as an orange early warning, and 1.2 is taken as a red early warning; sixthly, atomizing: 1.1 is taken as orange early warning, and 1.2 is taken as red early warning; and (c) freezing: and taking values according to weather conditions and line icing conditions. The specific grading is shown in attached table 8.
Preferably, the training of the preset power grid knowledge graph includes:
knowledge modeling, data extraction, knowledge extraction, map construction, storage optimization, knowledge reasoning and scene application.
The knowledge extraction comprises entity extraction, relation extraction and attribute extraction; the map construction comprises data cleaning, data normalization, semantic disambiguation, logic verification, format conversion and data ablation, and quality evaluation and knowledge reasoning are carried out after the data are processed; the data extraction comprises the following steps: risk identification quantization standard, Google map data and relational database user data; the storage optimization comprises a knowledge framework, a fusion knowledge base and function optimization, wherein the knowledge framework comprises rule files, user information, a workflow and industry knowledge, mining logic of the knowledge framework is constructed based on expert experience, a universal framework, a user framework and an industry framework are generated and then stored in a capacity and knowledge base, the capacity and knowledge base comprises triples, a graph database and a relational database, and finally, function optimization is carried out, wherein the function optimization comprises inverted indexes, increment updating, authority management, logic operation and a disaster tolerance mechanism.
S103, acquiring equipment information of power failure requirements, inputting the equipment information into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
and inputting power failure equipment requirement information, and traversing the power grid knowledge graph by adopting N-1 and N-2 fault sets. Analyzing a list of users affected by the power failure of the equipment and 10kV important users with reduced power supply reliability through the analysis of a topological structure of a knowledge graph power grid according to the equipment in a power failure plan; and obtaining the number of the power failure users and a list which are possibly caused after the power failure equipment has the N-1 fault through N-1 checking. And verifying the risk identification accuracy of the model according to manual experience. And automatically identifying the power failure plan risk according to the analysis of the power grid topological structure, and outputting the transformer substation voltage loss evaluation result, the risk level, the risk category and the analysis content of the dangerous points. The risk identification process is shown in FIG. 2.
And S104, generating a risk early warning notice according to the risk identification result.
The risk identification result comprises: fault type, hazard name, risk description, possible risk consequences, loss load (MW), loss load fraction (%), loss user number, loss user fraction (%), important user impact (total stop). And according to the power failure plan, the power grid risk level of each power failure plan can be obtained. Obtaining the following results according to a quantitative evaluation method of risk of the south China network: the risk values (risk severity score, social impact factor, loss load or user property factor; probability values (equipment type factor, fault category factor, historical data statistics factor), risk values, risk classes, risk pre-warning notice form 9.
The working principle of the future state power grid maintenance risk identification method based on the power grid knowledge graph is as follows: the preset power grid knowledge graph is established based on the existing power grid topological structure, and has the functions of power grid equipment relationship construction, human-computer interaction data query, hidden relationship reasoning and the like. Besides, the method also meets the requirements of information extraction, knowledge fusion and knowledge processing. The existing power grid knowledge graph can meet the following functions: specific information of each device in the power grid topology and the relation among the devices can be extracted from various types of data sources, and a body expression is formed on the basis. After new knowledge (such as new equipment information) is obtained, self-consistency can be realized, and contradiction and ambiguity between the new knowledge and historical data are eliminated. And for the fused new knowledge, data quality evaluation is given, and qualified parts are brought into a knowledge base, so that the quality of the knowledge map is ensured.
The embodiment of the application provides a future attitude electric wire netting overhauls risk identification device based on electric wire netting knowledge map, includes:
the first construction module 301 is configured to construct a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value;
a second construction module 302, configured to incorporate the grid overhaul risk identification logic data into the grid risk quantization index evaluation model based on a preset grid knowledge graph, and train the preset grid knowledge graph to associate the grid overhaul risk identification logic data with the preset grid knowledge graph, so as to construct a future state grid overhaul risk identification model based on the grid knowledge graph;
the calculation module 303 is configured to input equipment information for acquiring a power outage requirement into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
a generating module 304, configured to generate a risk early warning notice according to the risk identification result.
The working principle of the future state power grid maintenance risk identification device based on the power grid knowledge graph is that a first construction module 301 constructs a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value; the second construction module 302 brings the power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, and trains the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, so as to construct a future state power grid overhaul risk identification model based on the power grid knowledge graph; the calculation module 303 acquires equipment information of power failure requirements and inputs the equipment information into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result; the generating module 304 generates a risk pre-warning notice according to the risk identification result.
The embodiment of the application provides computer equipment, which comprises a processor and a memory connected with the processor;
the storage is used for storing a computer program, and the computer program is used for executing the power grid knowledge graph-based future state power grid overhaul risk identification method provided by any one of the above embodiments;
the processor is used to call and execute the computer program in the memory.
In summary, the invention provides a future state power grid maintenance risk identification method and device based on a power grid knowledge graph, and the method comprises the steps of constructing a power grid risk quantitative index evaluation model; training the preset power grid knowledge graph to associate power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph; and inputting the equipment information for acquiring the power failure requirement into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result and generate a risk early warning notice. The method is based on the existing blackout maintenance knowledge graph, stores the knowledge graph into the power grid safety risk quantitative evaluation standard, constructs a knowledge system for identifying the future state risk of the knowledge graph in the power grid blackout maintenance, and adopts a visualization technology to describe the excavation, analysis, construction and drawing of the knowledge graph on blackout maintenance risk information, so that a risk early warning notice result is output.
It is to be understood that the embodiments of the method provided above correspond to the embodiments of the apparatus described above, and the corresponding specific contents may be referred to each other, which is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. A future state power grid maintenance risk identification method based on a power grid knowledge graph is characterized by comprising the following steps:
constructing a power grid risk quantitative index evaluation model; the risk level corresponding to the power grid risk value is set in the power grid risk quantitative index evaluation model;
bringing power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, and training the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, so as to construct a future state power grid overhaul risk identification model based on the power grid knowledge graph;
equipment information for acquiring power failure requirements is input into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
and generating a risk early warning notice according to the risk identification result.
2. The method of claim 1, wherein the grid overhaul risk identification logic data comprises:
the power grid risk hazard severity quantitative index, the social influence factor quantitative index, the loss load or user property quantitative index, the equipment type factor and the historical statistical factor.
3. The method according to claim 1 or 2, wherein the training of the preset grid knowledge graph comprises:
knowledge modeling, data extraction, knowledge extraction, map construction, storage optimization, knowledge reasoning and scene application.
4. The method according to claim 3, wherein the equipment information for acquiring the power outage requirement is input into a power grid maintenance risk identification model of the power grid knowledge graph, and a risk identification result is obtained, and the method comprises the following steps:
inputting equipment information of power failure requirements to a power grid maintenance risk identification model of the power grid knowledge graph;
the power grid maintenance risk identification model of the power grid knowledge graph carries out power grid topological structure analysis on the equipment information and then carries out risk identification on the power failure demand;
and obtaining a risk identification result.
5. The method of claim 4, wherein the risk identification of the outage requirement comprises:
counting power failure users, identifying overlapped maintenance risks, identifying electrical island risks, identifying transformer substation voltage loss risks, identifying load transfer overload risks and identifying power grid risk point analysis risks.
6. The method of claim 1, wherein generating a risk pre-warning notice according to the risk identification result comprises:
and inputting the risk identification result into a list.
7. The method of claim 6, wherein the risk pre-warning notice comprises:
risk name, risk number, risk level, risk value, accident event level, risk start time, risk end time, and recommended action.
8. The utility model provides a future attitude electric wire netting overhauls risk identification device based on electric wire netting knowledge map which characterized in that includes:
the first construction module is used for constructing a power grid risk quantitative index evaluation model; the power grid risk quantitative index evaluation model is provided with a risk grade corresponding to a power grid risk value;
the second construction module is used for bringing power grid overhaul risk identification logic data into the power grid risk quantitative index evaluation model based on a preset power grid knowledge graph, training the preset power grid knowledge graph to associate the power grid overhaul risk identification logic data with the preset power grid knowledge graph, and constructing a future state power grid overhaul risk identification model based on the power grid knowledge graph;
the calculation module is used for acquiring equipment information of power failure requirements and inputting the equipment information into a power grid maintenance risk identification model of the power grid knowledge graph to obtain a risk identification result;
and the generating module is used for generating a risk early warning notice according to the risk identification result.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114707912A (en) * | 2022-06-01 | 2022-07-05 | 广东电网有限责任公司佛山供电局 | Power grid risk detection method, device and equipment |
| CN115577122A (en) * | 2022-11-09 | 2023-01-06 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power distribution network power failure information knowledge graph |
| CN117057590A (en) * | 2023-10-11 | 2023-11-14 | 国网山东省电力公司博兴县供电公司 | Power grid overhaul management system and method |
-
2022
- 2022-02-28 CN CN202210186577.0A patent/CN114548800A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114707912A (en) * | 2022-06-01 | 2022-07-05 | 广东电网有限责任公司佛山供电局 | Power grid risk detection method, device and equipment |
| CN114707912B (en) * | 2022-06-01 | 2022-08-19 | 广东电网有限责任公司佛山供电局 | Power grid risk detection method, device and equipment |
| CN115577122A (en) * | 2022-11-09 | 2023-01-06 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power distribution network power failure information knowledge graph |
| CN115577122B (en) * | 2022-11-09 | 2024-04-19 | 国网安徽省电力有限公司黄山供电公司 | Construction method of power outage information knowledge graph of power distribution network |
| CN117057590A (en) * | 2023-10-11 | 2023-11-14 | 国网山东省电力公司博兴县供电公司 | Power grid overhaul management system and method |
| CN117057590B (en) * | 2023-10-11 | 2024-02-02 | 国网山东省电力公司博兴县供电公司 | Power grid overhaul management system and method |
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