CN112991092B - Power-saving early warning information analysis method based on knowledge graph technology - Google Patents
Power-saving early warning information analysis method based on knowledge graph technology Download PDFInfo
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Abstract
The invention discloses a power-saving early-warning information analysis method based on a knowledge graph technology, which comprises the following steps of: s1, setting a target value: performing data association calculation analysis to obtain an early warning target value and displaying the early warning target value on a large screen; s2, demand analysis: establishing a power-saving early-warning information forming framework, establishing a power-saving early-warning information model based on a knowledge graph technology, and establishing a power-saving early-warning information analysis system; s3, overall planning; s4, developing a scheme: system function module development and data integration development; s5, embodiment. According to the power-saving early warning information analysis method based on the knowledge graph technology, the actual requirements of the power saving in the special area of the actual power grid are analyzed, prediction and early warning are used as main lines, decision support is provided for power saving users of the power grid, compatibility and coordination between the power saving users of the power grid and the power grid are improved, and the goals of power grid stability and user satisfaction are facilitated.
Description
Technical Field
The invention is applied to analysis of power-saving early-warning information, in particular to a power-saving early-warning information analysis method based on a knowledge graph technology.
Background
In recent years, along with the improvement of national international status, the large-scale activities accepted by the country are increased, the synchronous power grid electricity-protecting activities are increased, the annual electricity-protecting time is long, the electricity-protecting related range is wide, important activities, important periods and important user electricity-protecting works are performed, and the method is one of key works of power grid companies.
During the electricity protection period, special inspection, defect and hidden trouble investigation and treatment of power transmission, transformation and distribution equipment, investigation of internal faults or abnormal conditions of clients, site handling of emergency, information reporting and other works are required to be carried out, and the works are mainly completed manually at present, so that the following problems exist:
(1) Information transfer exists in a funnel: after the electricity-keeping task is decomposed, feedback information needs to be reported step by step, reaches the command center through 2-3 layers, and has time hysteresis. The hidden danger is reported through traditional modes such as telephone and the like to bring information transfer funnels.
(2) Line patrol personnel hidden danger investigation has blind area: on-site personnel cannot comprehensively observe defects and hidden dangers due to reasons such as standing positions and sight lines, and therefore judgment accuracy is affected.
(3) Manual prison dish efficiency remains to promote: the personnel on duty carries out remote monitoring through modes such as video that the system looked over, and control quantity is huge, and manual mode efficiency is lower and probably omits key information.
(4) The failure cause is not accurate and timely enough to be studied and judged: after the fault occurs, a plurality of groups of personnel need to visit on site to find the fault point, the accurate position and the fault cause can not be locked at the first time, and corresponding professionals are organized to carry materials and spare parts for repair.
In a comprehensive way, the power grid protection and power supply is enhanced, the economic loss caused by the power grid protection and power supply problem is reduced, the compatibility and coordination between the power grid protection and power supply users and the power grid are ensured by utilizing analysis prediction and early warning means, the economic benefit and the social benefit of the power grid protection and power supply users and power grid enterprises are improved, and the key problems of focus of important attention of people and urgent research and solution are achieved at present.
Disclosure of Invention
The invention aims to solve the technical problem of providing a power-saving early warning information analysis method based on a knowledge graph technology aiming at the defects of the prior art.
In order to solve the technical problems, the invention provides a power-saving early warning information analysis method based on a knowledge graph technology, which comprises the following steps:
s1, setting a target value: the method comprises the steps of importing a large amount of hidden danger, fault and equipment account data accumulated by the existing power grid power-protection related systems, performing data association calculation analysis on the background through a knowledge graph model, obtaining early warning target values and displaying the early warning target values on a large screen;
s2, demand analysis: establishing a power-saving early-warning information forming framework through data demand analysis, establishing a power-saving early-warning information model based on a knowledge graph technology through functional demand analysis, and establishing a power-saving early-warning information analysis system through system structure analysis;
s3, overall planning: investigation and collection of data, determination of overall requirements and provision of a feasibility scheme;
s4, developing a scheme: system function module development and data integration development; the development of a system functional module strictly follows a project implementation plan, details the functional requirements to be realized in each stage, and periodically submits the stage development results; and (3) data integration development, namely, integrated development of power grid basic data of the existing application system related to the system and data interaction between upper and lower units.
S5, embodiment: data verification, system deployment, commissioning, document writing and project summarization are performed.
As a possible implementation manner, further, the data demand analysis in step S2 is specifically to analyze the power-saving early-warning information composition and the power-saving early-warning information data type. The stage aims to establish and perfect the power-supply-protecting early warning information forming framework, study the data model and the calculation method based on the knowledge graph technology principle, and finally perform early warning prediction analysis.
As a possible implementation manner, further, the function requirement analysis in the step S2 specifically includes performing quantitative evaluation on the power-saving pre-warning information influence factor of the power grid, and obtaining an evaluation factor for evaluating the uncertainty of the power-saving pre-warning response event based on information extraction, knowledge fusion and quality evaluation of a knowledge graph technology. The functional demand analysis is to quantitatively evaluate the influence factors of the power-saving early-warning information of the power grid, acquire key index factors for producing expected events based on a knowledge graph, data classification and normalization method, and establish a power-saving early-warning information analysis system. Establishing a power-saving early warning information analysis system through function demand analysis; carrying out quantitative evaluation on the influence factors of the power-saving and power-supplying early warning information of the power grid, acquiring key index factors for producing expected events by a knowledge graph, data classification and normalization method, and establishing a power-saving and power-supplying early warning analysis model based on the knowledge graph; according to the requirements of a power grid company and power-saving users as guidance, a double-target model with maximized benefits and maximized user satisfaction of the power grid company is established, the whole process monitoring before, during and after power saving is ensured, risks are identified in advance, the occurrence rate of safety accidents is reduced, and the stable operation of the power grid is improved.
As a possible implementation manner, further, the system structure analysis of step S2 specifically includes: and checking faults and hidden dangers of the electricity-protecting equipment, constructing a parameterized knowledge graph model, carrying out early warning association calculation on equipment electricity-protecting core indexes, planning electricity-protecting areas, uploading documents, displaying a large screen and integrating a system.
As a possible implementation manner, in a process of quantitatively evaluating the influence factor of the power-saving and power-supplying early-warning information of the power grid, the voltage fluctuation, the oil temperature early-warning and the electric pressure value exceeding are converted into power grid early-warning response events, and the method includes: and quantitatively evaluating the power grid protection and power supply early warning information influence factors by adopting an information extraction link in a knowledge-based graph algorithm. The early warning information comprises event type and continuous type, the characteristics and requirements of the event type and the continuous early warning information are different, the event type and the continuous early warning information are sporadic and unpredictable, the continuous and predictable, and meanwhile, the influence mechanism, the caused hazard degree and the effect expression mode of sensitive equipment are different. Based on the characteristics, from the viewpoint of stable operation of the power grid, a general model based on the technical measures of the knowledge graph is researched and analyzed, the adaptation scene and the application method of the model are defined, and a general method for benefit evaluation during power supply protection is provided.
As a possible implementation manner, further, the power-saving early-warning information fluctuation influence factors comprise event-type influence factors and continuous-type influence factors; the event type influence factors include: a random lifting event; the continuous influence factor includes: the change of equipment load and the change of the operation mode of the power grid.
As a possible implementation manner, further, step S3 is specifically to complete standard formulation work of unified information collection, arrangement and data entry of relevant power grid equipment basic data and database model design scheme, and propose feasibility scheme of the project, and perform overall system design and database design.
The power-saving early warning model based on the knowledge graph comprises a high-dimensional joint probability supervision excitation function, and parameters of the model are identified through the high-dimensional joint probability supervision excitation function; the high-dimensional joint probability supervision excitation function is established by the following steps: based on real-time monitoring data and historical equipment hidden danger and fault data, an initialization probability supervision excitation function is established, identification training is carried out on highly relevant parameters in the probability supervision excitation function by adopting an elastic network regression-based method, and the high-dimensional joint probability supervision excitation function is established. When different power-saving early warning information acts on different types of power grid equipment, the early warning information forms different, so that model parameters cannot be directly obtained and have larger difference when the early warning content of the prediction equipment is evaluated by using a statistical analysis model. Therefore, checking and identifying parameters of the statistical analysis model is a key technology of the project, and the difficulty is as follows: the model parameters are influenced by various factors such as early warning data types, equipment operation production process and the like, and the parameters of the high-dimensional joint probability supervision excitation function are required to be checked and identified.
The improvement measures of the power supply early warning information comprise real-time monitoring data of various devices on one hand and adjustment of a power grid power supply scheme on the other hand. Correspondingly, the technical measures adopted and personnel investment are different, and the technical measures adopted need to be hooked in the aspects of power grid benefits, user satisfaction and the like. Therefore, the equipment faults, the reduced material loss during the power-saving period and other losses perceived by a user are comprehensively considered, and a power-saving early warning analysis universal model based on a knowledge graph is established based on statistics and artificial intelligence theory.
As a possible implementation manner, the power-saving early-warning information analysis system of step S2 further includes:
the electricity-protecting equipment fault and hidden trouble checking module is used for monitoring equipment monitored in the existing electricity-protecting period in real time, monitoring data of newly added equipment, and automatically carrying out data cleaning, integration and recording according to rules;
the parameterized knowledge graph model module is used for constructing a model according to a knowledge graph algorithm and carrying out quantitative configuration on key parameters of the model so as to meet the follow-up perfection work;
the equipment electricity-protecting core index early warning association calculation module is used for carrying out association early warning display based on related data of faults and hidden dangers of historical equipment models according to electricity-protecting core indexes focused by field personnel;
the power-saving area planning module is used for realizing the autonomous circle selection of the power-saving range according to the administrative area of the map, and labeling and displaying by the polygon to obtain the equipment data in the circle selection range;
the document uploading module is used for automatically generating standard documents for the power-saving early warning information and uploading the standard documents to a company storage server related date catalog in a timing way;
and the large screen display and system integration module is used for carrying out on-line display of early warning index information based on a large screen visualization module of the power protection platform and providing user-defined display content and display modes.
The invention adopts the technical scheme and has the following beneficial effects: according to the power-saving early warning information analysis method based on the knowledge graph technology, the actual requirements of the power saving in the special area of the actual power grid are analyzed, prediction and early warning are used as main lines, decision support is provided for power saving users of the power grid, compatibility and coordination between the power saving users of the power grid and the power grid are improved, and the goals of power grid stability and user satisfaction are facilitated.
Drawings
The invention is described in further detail below with reference to the attached drawings and detailed description:
FIG. 1 is a schematic flow chart of the method of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1, the invention provides a power-saving early warning information analysis method based on a knowledge graph technology, which comprises the following steps:
s1, setting a target value: leading in hidden danger, faults and equipment account data to perform data association calculation analysis to obtain an early warning target value;
s2, demand analysis: establishing a power-saving early-warning information forming framework through data demand analysis, establishing a power-saving early-warning information model based on a knowledge graph technology through functional demand analysis, and establishing a power-saving early-warning information analysis system through system structure analysis;
the power-saving early warning model based on the knowledge graph comprises a high-dimensional joint probability supervision excitation function, and parameters of the model are identified through the high-dimensional joint probability supervision excitation function; the high-dimensional joint probability supervision excitation function is established by the following steps: based on real-time monitoring data and historical equipment hidden danger and fault data, an initialization probability supervision excitation function is established, identification training is carried out on highly relevant parameters in the probability supervision excitation function by adopting an elastic network regression-based method, and the high-dimensional joint probability supervision excitation function is established.
S3, overall planning: investigation and collection of data, determination of overall requirements and provision of a feasibility scheme;
s4, developing a scheme: system function module development and data integration development;
s5, embodiment: data verification, system deployment, commissioning, document writing and project summarization are performed.
The power-saving early warning information comprises: transformer (oil chromatography early warning, iron core grounding current early warning, oil temperature early warning, voltage early warning, heavy load early warning, overload early warning), GIS combined electrical apparatus (pressure early warning, partial discharge early warning), arrester (resistive current early warning, leakage current early warning), transformer (current transformer capacitance early warning, voltage transformer capacitance early warning). The power supply early warning data types include: primary equipment data of a power main network, equipment basic data of an electric power distribution network, other equipment and system parameters. The power-saving early-warning information fluctuation influence factors comprise event-type influence factors and continuous-type influence factors; the event type influence factors include: a random lifting event; the continuous influence factor includes: the change of equipment load and the change of the operation mode of the power grid.
As a possible implementation manner, further, the data demand analysis in step S2 is specifically to analyze the power-saving early-warning information composition and the power-saving early-warning information data type.
As a possible implementation manner, further, the function requirement analysis in the step S2 specifically includes performing quantitative evaluation on the power-saving pre-warning information influence factor of the power grid, and obtaining an evaluation factor for evaluating the uncertainty of the power-saving pre-warning response event based on information extraction, knowledge fusion and quality evaluation of a knowledge graph technology.
As a possible implementation manner, further, the system structure analysis of step S2 specifically includes: and checking faults and hidden dangers of the electricity-protecting equipment, constructing a parameterized knowledge graph model, carrying out early warning association calculation on equipment electricity-protecting core indexes, planning electricity-protecting areas, uploading documents, displaying a large screen and integrating a system.
As a possible implementation manner, in a process of quantitatively evaluating the influence factor of the power-saving and power-supplying early-warning information of the power grid, the voltage fluctuation, the oil temperature early-warning and the electric pressure value exceeding are converted into power grid early-warning response events, and the method includes: and quantitatively evaluating the power grid protection and power supply early warning information influence factors by adopting an information extraction link in a knowledge-based graph algorithm.
As a possible implementation manner, further, the power-saving early-warning information fluctuation influence factors comprise event-type influence factors and continuous-type influence factors; the event type influence factors include: a random lifting event; the continuous influence factor includes: the change of equipment load and the change of the operation mode of the power grid.
As a possible implementation manner, further, step S3 is specifically to complete standard formulation work of unified information collection, arrangement and data entry of relevant power grid equipment basic data and database model design scheme, and propose feasibility scheme of the project, and perform overall system design and database design.
As a possible implementation manner, the power-saving early-warning information analysis system of step S2 further includes:
the electricity-protecting equipment fault and hidden trouble checking module is used for monitoring equipment monitored in the existing electricity-protecting period in real time, monitoring data of newly added equipment, and automatically carrying out data cleaning, integration and recording according to rules;
the parameterized knowledge graph model module is used for constructing a model according to a knowledge graph algorithm and carrying out quantitative configuration on key parameters of the model so as to meet the follow-up perfection work;
the equipment electricity-protecting core index early warning association calculation module is used for carrying out association early warning display based on related data of faults and hidden dangers of historical equipment models according to electricity-protecting core indexes focused by field personnel;
the power-saving area planning module is used for realizing the autonomous circle selection of the power-saving range according to the administrative area of the map, and labeling and displaying by the polygon to obtain the equipment data in the circle selection range;
the document uploading module is used for automatically generating standard documents for the power-saving early warning information and uploading the standard documents to a company storage server related date catalog in a timing way;
and the large screen display and system integration module is used for carrying out on-line display of early warning index information based on a large screen visualization module of the power protection platform and providing user-defined display content and display modes.
According to the power-saving early warning information analysis method based on the knowledge graph technology, the actual requirements of the power saving in the special area of the actual power grid are analyzed, prediction and early warning are used as main lines, decision support is provided for power saving users of the power grid, compatibility and coordination between the power saving users of the power grid and the power grid are improved, and the goals of power grid stability and user satisfaction are facilitated.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (4)
1. A power-saving early-warning information analysis method based on a knowledge graph technology is characterized by comprising the following steps of: the method comprises the following steps:
s1, setting a target value: leading in hidden danger, faults and equipment account data to perform data association calculation analysis to obtain an early warning target value;
s2, demand analysis: establishing power-saving early warning information through data demand analysis to form a framework;
establishing a power-saving early-warning information model based on a knowledge graph technology through functional demand analysis, wherein the functional demand analysis specifically carries out quantitative evaluation on power-saving early-warning information influence factors of a power grid, and acquires evaluation factors for evaluating uncertainty of power-saving early-warning response events based on information extraction, knowledge fusion and quality evaluation of the knowledge graph technology; acquiring key index factors for producing expected events based on a knowledge graph, data classification and normalization method; the power-saving early warning model based on the knowledge graph technology comprises a high-dimensional joint probability supervision excitation function, and parameters of the model are identified through the high-dimensional joint probability supervision excitation function; the high-dimensional joint probability supervision excitation function is established by the following steps: based on real-time monitoring data and historical equipment hidden danger and fault data, an initialization probability supervision excitation function is established, identification training is carried out on highly relevant parameters in the probability supervision excitation function by adopting a regression method based on an elastic network, and the high-dimensional joint probability supervision excitation function is established;
the power-saving early warning information analysis system is established through system structure analysis, and the system structure analysis specifically comprises the following steps: checking faults and hidden dangers of electricity-protecting equipment, constructing a parameterized knowledge graph model, carrying out early warning association calculation on equipment electricity-protecting core indexes, planning electricity-protecting areas, uploading documents, displaying a large screen and integrating a system; the power-saving early warning information analysis system comprises: the electricity-protecting equipment fault and hidden trouble checking module is used for monitoring equipment monitored in the existing electricity-protecting period in real time, monitoring data of newly added equipment, and automatically carrying out data cleaning, integration and recording according to rules;
the parameterized knowledge graph model module is used for constructing a model according to a knowledge graph algorithm and carrying out quantitative configuration on key parameters of the model so as to meet the follow-up perfection work;
the equipment electricity-protecting core index early warning association calculation module is used for carrying out association early warning display based on related data of faults and hidden dangers of historical equipment models according to electricity-protecting core indexes focused by field personnel;
the power-saving area planning module is used for realizing the autonomous circle selection of the power-saving range according to the administrative area of the map, and labeling and displaying by the polygon to obtain the equipment data in the circle selection range;
the document uploading module is used for automatically generating standard documents for the power-saving early warning information and uploading the standard documents to a company storage server related date catalog in a timed mode;
the large screen display and system integration module is used for carrying out on-line display of early warning index information based on a large screen visualization module of the power protection platform and providing user-defined display content and display modes;
s3, overall planning: investigation and collection of data, determination of overall requirements and provision of a feasibility scheme; the method comprises the steps of specifically completing standard formulation work of unified information collection, arrangement and data entry of relevant power grid equipment basic data, providing a feasibility scheme of an item, and carrying out overall system design and database design;
s4, developing a scheme: the system function module development and the data integration development, the system function module development strictly follows a project implementation plan, details the function requirements to be realized in each stage, and periodically submits the stage development results; data integration development, which is to perform integrated development on the basic data of the power grid of the existing application system related to the system and data interaction between the upper level unit and the lower level unit;
s5, embodiment: data verification, system deployment, commissioning, document writing and project summarization are performed.
2. The power-saving early warning information analysis method based on the knowledge graph technology according to claim 1, wherein the method is characterized by comprising the following steps of: the data demand analysis in the step S2 specifically includes analysis of the power-saving pre-warning information composition and the power-saving pre-warning information data type.
3. The power-saving early warning information analysis method based on the knowledge graph technology according to claim 1, wherein the method is characterized by comprising the following steps of: in the process of quantitatively evaluating the influence factors of the power-saving early-warning information of the power grid, the voltage fluctuation, the oil temperature early warning and the electric pressure value exceeding standard are converted into power grid early-warning response events, and the method comprises the following steps: and quantitatively evaluating the power grid protection and power supply early warning information influence factors by adopting an information extraction link in a knowledge-based graph algorithm.
4. The power-saving early-warning information analysis method based on the knowledge graph technology according to claim 3, wherein the method comprises the following steps of: the power-saving early warning information fluctuation influence factors comprise event type influence factors and continuous type influence factors; the event type influence factors include: a random lifting event; the continuous influence factor includes: the change of equipment load and the change of the operation mode of the power grid.
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| CN109657836A (en) * | 2018-11-19 | 2019-04-19 | 东莞理工学院 | A kind of Power System Analysis method for early warning based on big data |
| CN110825885A (en) * | 2019-11-13 | 2020-02-21 | 南方电网科学研究院有限责任公司 | Power equipment knowledge graph application system |
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| CN109657836A (en) * | 2018-11-19 | 2019-04-19 | 东莞理工学院 | A kind of Power System Analysis method for early warning based on big data |
| CN110825885A (en) * | 2019-11-13 | 2020-02-21 | 南方电网科学研究院有限责任公司 | Power equipment knowledge graph application system |
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