CN113869589A - Power transmission line accident prediction method based on knowledge graph and inspection system - Google Patents
Power transmission line accident prediction method based on knowledge graph and inspection system Download PDFInfo
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Abstract
The invention relates to a power transmission line accident prediction method and system based on a knowledge graph. Compared with the prior art, the method and the system realize the identification and early warning of the potential signals related to the power failure accident, and have the advantages of high digitization degree, high reliability, high accuracy and the like.
Description
Technical Field
The invention relates to the field of power transmission line accident prediction, in particular to a power transmission line accident prediction method and a routing inspection system based on a knowledge graph.
Background
The field of overhead transmission line operation and maintenance relates to line infrastructure, daily routing inspection, special inspection power conservation, loss work and the like, and a plurality of element information is accessed, fused and shared in the links, so that the problem that a power grid operation and maintenance manager cannot quickly and accurately predict the trend of hidden accidents and the situation of emergencies in real time and quickly and accurately according to the collected mass data service flows exists.
At present, a new method for discovering hidden dangers and predicting accidents from the digital analysis perspective is urgently needed, and accident hidden dangers are timely predicted from multiple and multidimensional potential precursors before different types of power failure accidents occur.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the power transmission line accident prediction method and the inspection system based on the knowledge graph, which have high digitization degree, high reliability and high accuracy.
The purpose of the invention can be realized by the following technical scheme:
according to the first aspect of the invention, the power transmission line accident prediction method based on the knowledge map is provided, the method constructs the digital knowledge map of the power transmission line service process for the daily operation and inspection service flow of the power transmission line, establishes the effective index of the overhead power transmission line knowledge, and timely predicts the accident potential based on the potential premonition before various power failure accident types occur.
Preferably, the process for constructing the digital knowledge graph of the service flow of the power transmission line specifically comprises the following steps:
the method comprises the steps of integrating data of a daily routing inspection database of the power transmission line and a block chain social routing inspection database, and constructing a digital knowledge map of a service flow of the power transmission line by performing series of operations of knowledge extraction, knowledge representation, entity alignment, knowledge updating, knowledge reasoning and quality assessment on the integrated database.
Preferably, the blockchain social tour inspection database includes structured data and unstructured data, wherein the unstructured data includes pictures and videos.
Preferably, the main body of the digital knowledge graph of the service flow of the power transmission line comprises an iron tower, staff, a geographical position, an accident incentive, an accident type and a dangerous state.
Preferably, the relationship between the main bodies of the electric transmission line service flow digital knowledge graph is automatically generated according to the statistical characteristics of the social routing inspection data.
Preferably, the anticipation of the accident type comprises recognition of abnormal conditions of personnel inspection problems and early warning recognition of potential tripping hazards.
Preferably, the identification of the abnormal situation of the personnel patrol problem specifically comprises the following steps:
by comparing the daily inspection data and the socialized inspection knowledge map of the staff, the abnormal condition of inspection of the staff is identified, particularly the potential danger state or accident state identified by the socialized inspection data is identified, and the quality evaluation of the inspection data of the staff is realized.
Preferably, the early warning identification of the potential trip hazard specifically includes:
based on the socialized routing inspection knowledge map, identifying the main bodies and the relations of the accident state and the dangerous state, and further early warning the possible hidden danger state;
and identifying the relation and the difference between the normal state and the hidden danger state by using a data mining analysis method, positioning the dangerous state elements and the relation in the knowledge map, and realizing the real-time early warning of the data hidden danger in the social routing inspection based on the knowledge map.
Preferably, for different types of power failure accidents, the routing inspection data in the past preset time period are traced through the block chain system, the relationship among different elements is mined, the generation reasons of different accidents are analyzed, common reasons and special reasons are found out, an accident element and relationship knowledge graph is established, and then a normal state, a dangerous state and an accident state are identified, so that the knowledge graph is established to identify and early warn early-stage signals related to the power failure accidents.
According to a second aspect of the present invention, there is provided a system based on the above-mentioned power transmission line accident prediction method based on the knowledge map, the system comprising:
the block chain-based patrol data acquisition module is used for acquiring daily patrol data and social patrol data of the power transmission line;
the knowledge map construction module is used for constructing a digital knowledge map of the service process of the power transmission line for the data acquired by the inspection data acquisition module;
the abnormal condition identification module is used for prejudging different accidents based on the knowledge graph;
and the abnormal condition processing module is used for carrying out emergency processing on the abnormal condition.
Compared with the prior art, the invention has the following advantages:
1) the power transmission line accident prediction method based on the knowledge graph, provided by the invention, can be used for timely predicting accident hidden dangers and carrying out multi-dimensional evaluation on the working effect of personnel in multiple and multi-dimensional potential precursors before different types of power failure accidents occur by constructing the digital knowledge graph of the service flow of the power transmission line;
2) the invention provides the data defining and analyzing requirements and the needed information system process correspondingly supported by the data defining and analyzing requirements through knowledge map modeling, and provides guarantee for comprehensive intelligent fusion analysis of data such as personnel inspection quality evaluation, accident prediction, early warning and the like;
3) the block chain technology adopted by the invention has the characteristics of decentralization, tamper resistance, debugging expandability and the like, reduces the interference behavior of people in data transmission, ensures that all tracing processes are controlled only by Internet equipment and programs, can assist in evidentiary clearing and provides powerful guarantee for the accuracy and credibility of original data.
Drawings
FIG. 1 is a schematic diagram of a digital knowledge graph construction of a transmission line service flow;
FIG. 2 is a schematic diagram of a social routing inspection system based on a block chain technology;
fig. 3 is a schematic diagram of a trip incident knowledge graph in an embodiment.
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 some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The embodiment provides a power transmission line accident prediction method based on a knowledge graph, which is characterized in that a power transmission line service flow digital knowledge graph is constructed for power transmission line daily operation and inspection service flows, effective indexes of overhead power transmission line knowledge are established, and accident potential hazards are predicted in time based on potential precursors before various power failure accident types occur.
A knowledge graph may be a relational network that links together all of the different kinds of information. Knowledge-graphs provide the ability to analyze problems from a "relational" perspective. The knowledge map provides guarantee for comprehensive intelligent fusion analysis of data such as personnel inspection quality evaluation, accident prediction and early warning. Fig. 3 presents a schematic diagram of a trip incident knowledge map.
As shown in fig. 1, the process of constructing the digital knowledge graph of the service flow of the power transmission line specifically includes:
the method comprises the steps of integrating data of a daily routing inspection database and a block chain social routing inspection database of the power transmission line, constructing a complex graph relation network, performing series of operations of knowledge extraction, knowledge representation, entity alignment, knowledge updating, knowledge reasoning and quality assessment on the integrated database, constructing a digital knowledge graph of the service flow of the power transmission line, evaluating the importance of a main body in the graph, and identifying the generation reason of an accident.
The block chain social tour inspection database comprises structured data and unstructured data, wherein the unstructured data comprises pictures and videos.
The knowledge extraction includes entity extraction, relationship extraction and attribute extraction.
The main body of the digital knowledge graph of the transmission line service process comprises an iron tower, staff, a geographical position, an accident incentive, an accident type and a dangerous state; the relationship between the main bodies is automatically generated according to the statistical characteristics of the social routing inspection data.
The accident type prejudgment comprises the identification of abnormal conditions of personnel inspection problems and the early warning identification of potential tripping hidden dangers.
1) The identification of abnormal conditions of the personnel inspection problems is specifically as follows:
by comparing the daily inspection data and the socialized inspection knowledge map of the staff, the abnormal condition of inspection of the staff is identified, particularly the potential danger state or accident state identified by the socialized inspection data is identified, and the quality evaluation of the inspection data of the staff is realized.
2) The early warning identification of the potential tripping hidden danger specifically comprises the following steps:
based on the socialized routing inspection knowledge map, identifying the main bodies and the relations of the accident state and the dangerous state, and further early warning the possible hidden danger state;
and identifying the relation and the difference between the normal state and the hidden danger state by using a data mining analysis method, positioning the dangerous state elements and the relation in the knowledge map, and realizing the real-time early warning of the data hidden danger in the social routing inspection based on the knowledge map.
For different types of power failure accidents, the routing inspection data in the past preset time period are traced through a block chain system, the relation among different elements is mined, the generation reasons of different accidents are analyzed, common reasons and special reasons are found out, an accident element and relation knowledge graph is established, and then a normal state, a dangerous state and an accident state are identified, so that the knowledge graph is established to identify and early warn early-stage signals related to the power failure accidents.
The system embodiment of the invention is given below, and a social patrol system based on the power transmission line accident prediction method based on the knowledge graph comprises the following steps:
the block chain-based patrol data acquisition module is used for acquiring daily patrol data and social patrol data of the power transmission line;
the knowledge map construction module is used for constructing a digital knowledge map of the service process of the power transmission line for the data acquired by the inspection data acquisition module;
the abnormal condition identification module is used for prejudging different accidents based on the knowledge graph;
and the abnormal condition processing module is used for carrying out emergency processing on the abnormal condition.
The block chain is used as an encrypted distributed database ledger, and the encrypted distributed database system design realizes the traceable and non-falsifiable characteristics of data.
The block chain-based social inspection system achieves social collection of a large amount of data of anti-external-damage dangerous states. Through comparing the block chain anti-external damage data with the patrol data of the staff, the evaluation of the patrol state of the staff and the backtracking and early warning of the dangerous or accident state can be realized.
In addition, the blockchain technology is a brand new distributed infrastructure and computing paradigm, adopts a blockchain data structure to verify and store data, utilizes a distributed node consensus algorithm to generate and update data, utilizes a cryptographic manner to ensure the security of data transmission and access, and utilizes an intelligent contract composed of automated script codes to program and operate data. The transaction data generated by each participating main body in the block chain can be packed into a data block, the data blocks are sequentially arranged according to the time sequence to form the chain of the data blocks, any information modification can be carried out only by a main body consent party with an appointed proportion, and only new information can be added, and old information cannot be deleted or modified. Each participating main body has the same data chain and cannot be tampered unilaterally; the information sharing and the consistent decision among the main bodies ensure that the identity of each main body and the transaction information among the main bodies cannot be falsified and are public and transparent; interference behaviors of people in data transmission are reduced, and all tracing processes are controlled only by internet equipment and programs and can be verified to be clear.
The blockchain technology is essentially a distributed account book, and in the line anti-outages, incentives can be realized by means of point exchange. The block chain technology is not modifiable and has traceability, a public, transparent and fair system is provided, the reward points obtained by social personnel are restricted by intelligent contracts, and inquireable records can influence other audience groups which do not participate or possibly participate in anti-outages through social networks, so that certain demonstration effect is achieved.
The method comprises the steps of establishing an accident disposal resource allocation model based on limited resource investment for an abnormal condition processing module in the system through a digital knowledge graph of a service flow of the power transmission line, performing emergency disposal on accident hidden dangers in daily routing inspection data of the power transmission line, and specifically comprises the steps of evaluating the importance of accidents or hidden danger accidents based on a knowledge graph and establishing a resource allocation model based on geographic positions and disposal priority.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A power transmission line accident prediction method based on a knowledge graph is characterized in that the method constructs a digital knowledge graph of a power transmission line service flow for a power transmission line daily operation and inspection service flow, establishes an effective index of overhead power transmission line knowledge, and timely predicts accident hidden dangers based on potential precursors before various power failure accident types occur.
2. The power transmission line accident prediction method based on the knowledge graph according to claim 1, wherein the construction process of the digital knowledge graph of the service flow of the power transmission line specifically comprises the following steps:
the method comprises the steps of integrating data of a daily routing inspection database of the power transmission line and a block chain social routing inspection database, and constructing a digital knowledge map of a service flow of the power transmission line by performing series of operations of knowledge extraction, knowledge representation, entity alignment, knowledge updating, knowledge reasoning and quality assessment on the integrated database.
3. The knowledge-graph-based power transmission line accident prediction method according to claim 2, wherein the block-chain social routing inspection database comprises structured data and unstructured data, wherein the unstructured data comprises pictures and videos.
4. The power transmission line accident prediction method based on the knowledge graph of claim 2, wherein the main body of the power transmission line business process digital knowledge graph comprises an iron tower, staff, a geographical position, accident inducement, an accident type and a dangerous state.
5. The power transmission line accident prediction method based on the knowledge graph according to claim 4, wherein the relationship between the main bodies of the digital knowledge graph of the power transmission line service process is automatically generated according to the statistical characteristics of the social patrol data.
6. The power transmission line accident prediction method based on the knowledge graph of claim 1, wherein the prediction of the accident type comprises recognition of abnormal conditions of personnel inspection problems and early warning recognition of potential tripping hazards.
7. The power transmission line accident prediction method based on the knowledge graph according to claim 6, wherein the identification of the abnormal situation of the personnel inspection problems is specifically as follows:
by comparing the daily inspection data and the socialized inspection knowledge map of the staff, the abnormal condition of inspection of the staff is identified, particularly the potential danger state or accident state identified by the socialized inspection data is identified, and the quality evaluation of the inspection data of the staff is realized.
8. The power transmission line accident prediction method based on the knowledge graph as claimed in claim 6, wherein the early warning identification of the potential trip hazard is specifically as follows:
based on the socialized routing inspection knowledge map, identifying the main bodies and the relations of the accident state and the dangerous state, and further early warning the possible hidden danger state;
and identifying the relation and the difference between the normal state and the hidden danger state by using a data mining analysis method, positioning the dangerous state elements and the relation in the knowledge map, and realizing the real-time early warning of the data hidden danger in the social routing inspection based on the knowledge map.
9. The power transmission line accident prediction method based on the knowledge graph as claimed in claim 6, wherein for different types of power failure accidents, patrol data in a past preset time period are traced through a block chain system, relationships among different elements are mined, generation causes of different accidents are analyzed, common causes and special causes are found out, accident element and relationship knowledge graphs are established, and then normal states, dangerous states and accident states are identified, so that the knowledge graph is established to identify and early warn early signals related to the power failure accidents.
10. An inspection system based on the knowledge-graph-based power transmission line accident prediction method of claim 1, characterized by comprising:
the block chain-based patrol data acquisition module is used for acquiring daily patrol data and social patrol data of the power transmission line;
the knowledge map construction module is used for constructing a digital knowledge map of the service process of the power transmission line for the data acquired by the inspection data acquisition module;
the abnormal condition identification module is used for prejudging different accidents based on the knowledge graph;
and the abnormal condition processing module is used for carrying out emergency processing on the abnormal condition.
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| CN117079044A (en) * | 2023-08-25 | 2023-11-17 | 华大天元(北京)科技股份有限公司 | Training method, early warning method and device for recognition model of external force damage of overhead line |
| CN117114412A (en) * | 2023-09-12 | 2023-11-24 | 瑞丰宝丽(北京)科技有限公司 | Safety pre-control method and device for dangerous chemical production enterprises |
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