CN113420162A - Equipment operation chain state monitoring method based on knowledge graph - Google Patents

Equipment operation chain state monitoring method based on knowledge graph Download PDF

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CN113420162A
CN113420162A CN202110705816.4A CN202110705816A CN113420162A CN 113420162 A CN113420162 A CN 113420162A CN 202110705816 A CN202110705816 A CN 202110705816A CN 113420162 A CN113420162 A CN 113420162A
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equipment
state
information
knowledge
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CN113420162B (en
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韩强
林润
李平
徐元孚
于洋
许雷
王鑫
刘圣楠
杜明
袁中琛
武江
白静洁
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Beijing Kedong Electric Power Control System Co Ltd
State Grid Electric Power Research Institute
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Beijing Kedong Electric Power Control System Co Ltd
State Grid Electric Power Research Institute
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • 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
    • 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
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • 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
    • G06F16/34—Browsing; Visualisation therefor
    • 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
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • 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
    • 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
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • 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 knowledge graph-based equipment operation chain state monitoring method, which comprises the following specific working steps of: collecting device information directly and indirectly associated with a target device in an electrical grid; preprocessing and evaluating the collected equipment information; constructing an operation state knowledge graph of the target equipment according to the preprocessing and evaluation results; further creating a state operation chain of the target equipment, carrying out comprehensive analysis on the state operation chain of the target equipment, determining the weight of the data index and obtaining a state evaluation result of the equipment state operation chain; ranking the attention degrees of a plurality of target devices in the device state operation chain according to the evaluation result of the device operation chain state, and actively recommending device information according to the ranking of the attention degrees; according to the invention, a dynamically updated knowledge map library is established in the power system, so that a real-time target equipment operation chain is established, and the requirements of a user for timely acquiring the equipment operation state trend and guiding the user to regulate and control to carry out real-time auxiliary decision making are met.

Description

Equipment operation chain state monitoring method based on knowledge graph
Technical Field
The invention belongs to the field of monitoring of power system operation scheduling equipment, and particularly relates to an equipment operation chain state monitoring method based on a knowledge graph.
Background
With the comprehensive implementation of the gradual promotion of power grid intellectualization and the integration of regulation and control, the characteristics of multiple sources, high dimension, prior and isomerism of large electric power data are increasingly prominent, the decision and operation pressure of regulation and control operators is gradually increased, and the real-time state detection of the power grid regulation and control is extremely urgent.
The traditional equipment running state monitoring mainly depends on static information such as manual maintenance equipment management parameters and the like, multi-source service data such as power grid equipment running information, external environment information, video monitoring information, micro-terrain information, power transmission and transformation on-line monitoring information, equipment three-dimensional model information and the like are not fully fused, and completeness coverage of data sources cannot be achieved. And the evaluation of the running state of the equipment is mainly based on regular evaluation, and the requirements of timely acquiring the running state trend of the equipment and guiding regulation and control to carry out real-time auxiliary decision making cannot be met, so that the decision making of the power grid regulation and control mostly depends on a disposal plan and an expected fault set which are made offline by manual experience, and the existing auxiliary decision making system is lack of adaptive response capability.
Disclosure of Invention
The purpose of the invention is as follows: aiming at multi-source data such as maintenance, operation, defects, operation, measurement, topology and the like, the method establishes a dynamically updated knowledge map library and constructs a target equipment operation chain with real-time mapping, meets the requirement of timely acquiring equipment operation state trend and guiding regulation and control to carry out real-time auxiliary decision, and accordingly promotes priority scheduling among equipment in a power grid and processing of accuracy and availability of target equipment data.
The technical scheme is as follows: the method for monitoring the equipment operation chain state based on the knowledge graph comprises the following steps:
collecting device information associated with target devices by taking the target devices to be monitored as a center;
constructing a knowledge graph of the running state of the target equipment according to the equipment information;
constructing an equipment operation chain based on the knowledge graph, monitoring the operation state of the target equipment and comprehensively analyzing the equipment operation chain to obtain the state evaluation results of the target equipment and the equipment operation chain;
ordering attention degrees of the target equipment and the equipment operation chain according to the evaluation result, and pushing equipment and the equipment operation chain which need to be focused according to the attention degrees;
the equipment operation chain refers to equipment which takes target equipment as a center and has direct and indirect relation with the equipment, and comprises operation topology of the equipment, secondary devices (such as measurement and protection devices) and equipment which influences the operation state of the target equipment according to experience judgment of field workers.
In a further embodiment, the device information comprises:
static parameters: the static parameter is not changed after self-recording and is used as a reference quantity for judging the equipment state index;
dynamic parameters: the method comprises the steps of synchronously updating monitoring parameters reflecting the health state and risk of equipment during running;
quasi-dynamic parameters: including periodically updated overhaul, defect, and fault parameters;
external parameters: including meteorological parameters, environmental parameters, and economic parameters.
In a further embodiment, a method for building a knowledge-graph of the operational status of a target device from device information includes:
extracting knowledge of entities, attributes and relationships from the equipment information;
and carrying out reasoning and constructing a knowledge graph through a data mining algorithm according to the knowledge extraction result.
In a further embodiment, prior to the extraction of knowledge of device information entities, attributes and relationships, the textual data and non-textual data of the device information are processed as follows:
arranging the non-text data in the equipment information to form text data;
and processing the text data in the equipment information and the text data formed after the arrangement according to the non-text data by using a missing value, an abnormal value, a repeated value and dirty data so as to standardize the text data.
In a further embodiment, the method of knowledge extraction comprises:
carrying out knowledge extraction on equipment information with different sources and different structures to form structured data;
through recognition, understanding, screening and formatting, all knowledge points in the structured data are extracted and stored, so that semantic annotation of the existing unstructured information is realized.
In further embodiments, the knowledge extraction includes an entity extraction, a relationship extraction, and an event extraction;
the entity extraction includes detection and classification of entities;
the relation extraction is four-tuple extraction, and further is used for extracting various relations among entities and/or between the entities and attributes in a time axis mode, namely semantic relations among associated entities and/or between the entities and the attributes are obtained in real time; the basic units of the knowledge graph are as follows: an "entity-relationship-entity" and/or an "entity-relationship-attribute" triple; according to the invention, a time axis attribute is added on the basis of the triple to form a quadruple, the progressive sequence of the time axis is taken as the dimension of extraction and update, and the dynamic update is carried out on each state data of the equipment in the power grid in real time.
In a further embodiment, the event extraction is extraction of a multi-element relation, and the unstructured text data containing the event information is presented in the form of structured text data; in a further embodiment, the method for building a knowledge graph of the operating state of the target device further comprises: and introducing updated equipment information periodically, comparing the equipment information with the stored equipment information, and updating the knowledge graph according to the comparison result.
In a further embodiment, the equipment information periodically imported and updated includes data information of the D5000, the regulation cloud and the OMS system, and changes of the plant station and the equipment obtained by comparison.
In a further embodiment, the plant operation chain is created by mapping according to the grid topology stored in the operation state knowledge graph of the target plant. In a further embodiment, the state evaluation result is obtained by measuring the fluctuation of indexes by using an entropy method and analyzing the overhaul and defect conditions of an equipment operation chain for three months by combining a knowledge graph, so that the qualitative and quantitative evaluation of the equipment state is realized, and the attention degree is increased according to the overhaul and defect times;
when the fluctuation of the index is measured by using an entropy method, the larger the value is, the larger the dispersion of the index is, and the larger the corresponding weight is;
firstly, acquiring original data of equipment state indexes to form an original data matrix with m groups of data and n evaluation indexes, carrying out column-based normalization processing on the matrix, and solving the proportion of each item to the same type of indexes, wherein the calculation formula is as follows:
Figure BDA0003131197740000041
wherein x isijA value representing the jth index in the ith set of sample samples;
further, an entropy k of a j index is calculatedjThe formula is as follows:
Figure BDA0003131197740000042
wherein p is 1/ln (m) is a constant, yijDenotes xijFor the specific gravity of the similar indexes, the weight formula for calculating the jth index is as follows:
Figure BDA0003131197740000051
all index weights of the equipment can be obtained through formulas (1), (2) and (3), and an evaluation result is determined by combining the current equipment operation chain state and the weights.
In a further embodiment, pushing the devices and device operation chains with important attention includes the following:
and (3) comprehensively evaluating the result and the attention degree of the target equipment in the operation chain, and automatically pushing the target equipment needing important attention and the operation chain of the target equipment for the user by combining the specialty, the post and the responsibility of the user, and pushing the state evaluation result and the defect repairing times of the operation chain for the assistant decision of a dispatcher.
Has the advantages that: compared with the prior art, the invention has the following advantages:
(1) according to the method, a dynamically updated knowledge map library is established and a real-time mapping target equipment operation chain is constructed according to multi-source data such as overhaul, operation, defects, operation, measurement, topology and the like, so that the requirements of timely acquiring equipment operation state trends and guiding regulation and control to carry out real-time auxiliary decision making are met, and therefore priority scheduling among equipment in a power grid and processing on the accuracy and the availability of target equipment data are improved.
(2) The method has the advantages that the equipment state change is displayed by using a visualization technology, the equipment states and the mutual relation among the equipment states are mined, analyzed, constructed and drawn, the real-time monitoring of the operation state of the target equipment is realized by constructing the state monitoring of the operation chain of the target equipment based on the knowledge map, the more comprehensive system display and analysis of the power grid operation equipment are facilitated, powerful basis is provided for the equipment health state evaluation, risk assessment and fault research and judgment and early warning of a dispatcher, and the potential operation risk of key equipment, namely the target equipment is reduced.
(3) For the running states of faults, defects, alarms and the like of target equipment stored at regular time, samples of the target equipment in power grid dispatching are added, effective support is provided for state risk assessment and fault analysis of the target equipment, training of an artificial intelligence model is supported, and the requirement for monitoring the running state of the equipment is met.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic flow chart of an embodiment of the method of the present invention.
Fig. 3 is a flowchart of a work flow of constructing a knowledge graph according to an operation chain state of a target device in a power grid.
Detailed Description
In order to more fully understand the technical content of the present invention, the technical solution of the present invention will be further described and illustrated with reference to the following specific embodiments, but not limited thereto.
The method for monitoring the chain state of the equipment based on the knowledge graph is further explained by combining with the figure 1:
collecting equipment information directly and indirectly associated with target equipment to be monitored in a power grid by taking the target equipment as a center, and performing data preprocessing and quality evaluation;
constructing a knowledge graph of the running state of the target equipment according to the equipment information;
constructing an equipment operation chain based on the knowledge graph, monitoring the operation state of the target equipment and comprehensively analyzing the equipment operation chain to obtain the state evaluation results of the target equipment and the equipment operation chain;
ordering attention degrees of the target equipment and the equipment operation chain according to the evaluation result, and pushing equipment and the equipment operation chain which need to be focused according to the attention degrees;
the equipment operation chain refers to equipment which takes target equipment as a center and has direct and indirect relation with the equipment, and comprises operation topology of the equipment, secondary devices (such as measurement and protection devices) and equipment which influences the operation state of the target equipment according to experience judgment of field workers.
The method provided by the invention has the advantages that the equipment operation chain is constructed by utilizing the knowledge map technology, the state of the equipment operation chain is monitored, the more comprehensive system display and analysis of the power grid operation equipment are facilitated, and a powerful basis is provided for the equipment health state evaluation, the risk evaluation and the fault study and judgment and early warning of a dispatcher.
The multivariate multidimensional data source is a basic condition for carrying out equipment state monitoring analysis; the equipment information comprises various data, records and the like suitable for equipment health state and risk assessment in the whole life cycle range of the equipment, and all data can be divided into four major parameters according to the updating frequency and the data source. The specific device information classification includes: static parameters, dynamic parameters, quasi-dynamic parameters and external parameters;
the static parameters include: the method comprises the steps of (1) machine account parameters of target equipment and test parameters before the target equipment is put into operation; the static parameters are not changed after being recorded, and are used as reference values and judgment bases for evaluating certain state parameters, and are used as initial values, limiting threshold values and the like.
The dynamic parameters include: running recorded data, inspection recorded parameters, live detection parameters and online monitoring parameters; all the dynamic parameters are periodically acquired according to the running state of the target equipment and then updated; the dynamic parameter data updating period is short, the timeliness is good, the most main and key equipment state evaluation data source is provided, and the health state and the risk of the equipment can be reflected most timely.
The quasi-dynamic parameters include: examining and repairing test parameters, defects and fault parameters; the quasi-dynamic parameters are usually acquired and updated periodically or aperiodically, and the period is usually in the unit of months; compared with dynamic parameters, the time efficiency of the dynamic parameters is relatively poor, but the dynamic parameters play a key role in the accuracy of equipment state evaluation; the quasi-dynamic parameter is particularly important when a multi-dimensional device state evaluation method is adopted to analyze potential specific defects/faults of the device.
The external parameters include: meteorological factors, environmental factors, and socioeconomic factors; the meteorological factors mainly comprise temperature, wind power, precipitation and the like; the environmental factors mainly include: earthquakes, floods, and the like; the socioeconomic factors mainly include: electricity consumption, economic situation, etc.
The state evaluation result of the further target equipment operation chain is characterized in that the qualitative and quantitative evaluation of the equipment state is realized by measuring the index fluctuation by using an entropy method and analyzing the overhaul and defect conditions of the equipment operation chain for three months by combining a knowledge graph, and the attention degree is increased according to the overhaul and defect times;
when the fluctuation of the index is measured by using an entropy method, the larger the value is, the larger the dispersion of the index is, and the larger the corresponding weight is;
firstly, acquiring original data of equipment state indexes to form an original data matrix with m groups of data and n evaluation indexes, carrying out column-based normalization processing on the matrix, and solving the proportion of each item to the same type of indexes, wherein the calculation formula is as follows:
Figure BDA0003131197740000081
wherein x isijA value representing the jth index in the ith set of sample samples;
further, an entropy k of a j index is calculatedjThe formula is as follows:
Figure BDA0003131197740000082
wherein p is 1/ln (m) is a constant, yijDenotes xijFor the specific gravity of the similar indexes, the weight formula for calculating the jth index is as follows:
Figure BDA0003131197740000083
all index weights of the equipment can be obtained through formulas (1), (2) and (3), and an evaluation result is determined by combining the current equipment operation chain state and the weights.
Pushing the equipment and the equipment operation chain which need to pay important attention comprises the following steps:
and (3) comprehensively evaluating the result and the attention degree of the target equipment in the operation chain, and automatically pushing the target equipment needing important attention and the operation chain of the target equipment for the user by combining the specialty, the post and the responsibility of the user, and pushing the state evaluation result and the defect repairing times of the operation chain for the assistant decision of a dispatcher.
An embodiment of a method for monitoring the chain state of equipment operation based on a knowledge graph is described with reference to fig. 2, which comprises the following working steps:
collecting device information associated with target devices by taking the target devices to be monitored as a center;
preprocessing and evaluating the collected equipment information; key data information of system influence equipment operation such as EMS, OMS, big data monitoring, incident monitoring, cloud regulation and control, power grid topology, fault recording and the like is combed;
the method comprises the steps of constructing an operation state knowledge graph of target equipment for collected equipment information after multi-dimensional research by carrying out data processing, knowledge extraction, knowledge fusion and knowledge updating on the collected equipment data information;
in this embodiment, taking a transformer as an example, the key data information affecting the operation of the transformer includes:
Figure BDA0003131197740000091
Figure BDA0003131197740000101
taking a transformer as a center, combing the topological structure relationship of the equipment in the power grid, and storing the data of the transformer equipment information after data preprocessing and the topological structure relationship of the equipment in the power grid into an operation state knowledge graph of the transformer; creating a state operating chain for mapping the target equipment of the equipment based on the operating state knowledge graph of the equipment;
comprehensively analyzing an operation chain taking the transformer as a center, determining equipment information index weight and obtaining a state evaluation result of the operation chain of the transformer or equipment associated with the transformer;
and sequencing the attention degrees of a plurality of associated devices in the state operation chain with the transformer as the center according to the obtained evaluation result of the state of the operation chain, and actively recommending the device information according to the sequencing of the attention degrees.
In the present invention, the state operation chain of the target device is created and mapped according to a power grid topological relation stored in an operation state knowledge graph of the target device, for example, the target device in the present invention may include: the target device body, the target device accessory, a switch connected with the target device, a knife switch, a protection device, a signal channel and other related devices.
A method for constructing a knowledge graph of the operation state of the target device according to the device information is described with reference to fig. 3:
processing text data and non-text data of the data pair equipment information before entity extraction and relation extraction; the method comprises the following steps of (1) realizing the inspection and verification of text data and non-text data in the power grid;
arranging the non-text data in the equipment information to form text data;
processing the text data in the equipment information and the text data formed after the arrangement according to the non-text data by using a missing value, an abnormal value, a repeated value and dirty data to standardize the text data;
the non-textual data is equipment troubleshooting, equipment defects, and reports, tables, and pictures generated during operation of the equipment.
And finally, data processing such as redundant data cleaning, abnormal data processing and the like is realized, and the period and time for constructing data marks are reduced.
The knowledge extraction specifically comprises the steps of carrying out entity extraction, relation extraction and event extraction on data subjected to data preprocessing;
the entity extraction includes detection and classification of entities;
the relation extraction is four-tuple extraction, and further is to extract various relations among the entities in a time axis mode, namely to obtain semantic relations among the associated entities and/or between the entities and the attributes in real time; the basic units of the knowledge graph are as follows: an "entity-relationship-entity" and/or an "entity-relationship-attribute" triple; according to the invention, a time axis attribute is added on the basis of the triple to form a quadruple, the progressive sequence of the time axis is taken as the dimension of extraction and update, and the dynamic update is carried out on each state data of the equipment in the power grid in real time.
The event extraction is the extraction of a multivariate relation, and the unstructured text data containing event information is presented in the form of structured text data;
the knowledge extraction specifically comprises the following steps:
carrying out knowledge extraction on equipment information with different sources and different structures to form structured data;
extracting each knowledge point in the structured data for storage through identification, understanding, screening and formatting to realize semantic annotation of the existing unstructured information and further complete knowledge fusion;
the knowledge fusion in the invention means that the events such as defects, maintenance and the like are taken as storage targets, multi-source knowledge is fused, a knowledge system is constructed, and a knowledge graph of the running state of target equipment is constructed.
According to the method, a dynamically updated knowledge map library is established and a real-time mapping target equipment operation chain is constructed according to multi-source data such as overhaul, operation, defects, operation, measurement, topology and the like, so that the requirements of timely acquiring equipment operation state trends and guiding regulation and control to carry out real-time auxiliary decision making are met, and therefore priority scheduling among equipment in a power grid and processing on the accuracy and the availability of target equipment data are improved.
Secondly, the change of the equipment state is displayed by using a visualization technology, the equipment state and the mutual relation of the equipment state are mined, analyzed, constructed and drawn, the real-time monitoring of the operation state of the target equipment is realized by constructing the state monitoring of the operation chain of the target equipment based on the knowledge graph, the more comprehensive system display and analysis of the power grid operation equipment are facilitated, powerful basis is provided for the equipment health state evaluation, risk evaluation and fault research and judgment and early warning of a dispatcher, and the potential operation risk of key equipment, namely the target equipment is reduced.
Finally, for the running states of faults, defects, alarms and the like of the target equipment stored at regular time, samples of the target equipment in power grid dispatching are added, effective support is provided for state risk assessment and fault analysis of the target equipment, training of an artificial intelligence model is supported, and the requirement for monitoring the running state of the equipment is met.
The present invention has been described above by way of illustration in the drawings, and it will be understood by those skilled in the art that the present disclosure is not limited to the embodiments described above, and various changes, modifications and substitutions may be made without departing from the scope of the present invention.

Claims (10)

1. A method for monitoring the running state of power grid dispatching equipment is characterized by comprising the following steps:
collecting device information associated with target devices by taking the target devices to be monitored as a center;
constructing a knowledge graph of the running state of the target equipment according to the equipment information;
constructing an equipment operation chain based on the knowledge graph, monitoring the operation state of the target equipment and comprehensively analyzing the equipment operation chain to obtain the state evaluation results of the target equipment and the equipment operation chain;
ordering attention degrees of the target equipment and the equipment operation chain according to the evaluation result, and pushing equipment and the equipment operation chain which need to be focused according to the attention degrees;
the equipment operation chain refers to equipment which takes target equipment as a center and has direct and indirect relations with the equipment, and comprises operation topology of the equipment, a secondary device and the equipment which influences the operation state of the target equipment.
2. The method for monitoring the operation state of the power grid dispatching equipment according to claim 1, wherein the equipment information comprises:
static parameters: the static parameter is not changed after self-recording and is used as a reference quantity for judging the equipment state index;
dynamic parameters: the method comprises the steps of synchronously updating monitoring parameters reflecting the health state and risk of equipment during running;
quasi-dynamic parameters: including periodically updated overhaul, defect, and fault parameters;
external parameters: including meteorological parameters, environmental parameters, and economic parameters.
3. The method for monitoring the operating state of the power grid dispatching equipment according to claim 1, wherein the method for constructing the knowledge graph of the operating state of the target equipment according to the equipment information comprises the following steps:
extracting knowledge of entities, attributes and relationships from the equipment information;
and carrying out reasoning and constructing a knowledge graph through a data mining algorithm according to the knowledge extraction result.
4. The method for monitoring the operating state of the power grid dispatching equipment according to claim 3, wherein before the knowledge of the equipment information entities, attributes and relationships is extracted, the following processing is performed on the text data and the non-text data of the equipment information:
arranging the non-text data in the equipment information to form text data;
and processing the text data in the equipment information and the text data formed after the arrangement according to the non-text data by using a missing value, an abnormal value, a repeated value and dirty data so as to standardize the text data.
5. The method for monitoring the operation chain state of knowledge-graph-based equipment according to claim 3,
the knowledge extraction method comprises the following steps:
carrying out knowledge extraction on equipment information with different sources and different structures to form structured data;
through recognition, understanding, screening and formatting, all knowledge points in the structured data are extracted and stored, so that semantic annotation of the existing unstructured information is realized.
6. The method for monitoring the operation chain state of the knowledge-graph-based equipment according to claim 5, wherein the knowledge extraction comprises entity extraction, relation extraction and event extraction;
the entity extraction includes detection and classification of entities;
the relation extraction is to acquire semantic relations among the associated entities and/or between the entities and the attributes in real time;
the event extraction is to present the unstructured text data containing the event information in the form of structured text data.
7. The method for monitoring the operation state of the power grid dispatching equipment as claimed in claim 3, wherein the method further comprises:
and introducing updated equipment information periodically, comparing the equipment information with the stored equipment information, and updating the knowledge graph according to the comparison result.
8. The method for monitoring the running state of the power grid scheduling device according to claim 7, wherein the periodically imported updated device information includes D5000, regulation cloud, and OMS system data information, and plant station and device change conditions obtained by comparison.
9. The method for monitoring the operating state of the power grid dispatching equipment according to any one of claims 1 to 8, wherein the equipment operation chain is created and mapped according to a power grid topological relation stored in an operating state knowledge graph of target equipment.
10. The method for monitoring the running state of the power grid dispatching equipment according to claim 1, wherein the state evaluation result is obtained by measuring index fluctuation by using an entropy method and analyzing the overhaul and defect conditions of the running chain of the equipment for three months by combining a knowledge graph, so that qualitative and quantitative evaluation of the equipment state is realized, and the attention degree is increased according to the overhaul and defect times.
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