CN116011827A - Power failure monitoring analysis and early warning system and method for key cells - Google Patents
Power failure monitoring analysis and early warning system and method for key cells Download PDFInfo
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Abstract
The invention discloses a power failure monitoring analysis and early warning system and method for key cells, wherein the system comprises a database server, a key cell analysis and identification module, a power failure data acquisition module, a power failure event analysis module, a power failure risk monitoring module, a power failure early warning module and an emergency plan making module; in addition, various power failure events of the key cell are monitored in real time; the power failure early warning is carried out according to the data and the monitoring result, the traditional passive processing mode is abandoned, the power failure early warning is carried out for customers of the key district in time, the effects of reporting the power failure information in real time and accurately analyzing the power failure information to the home are achieved, corresponding emergency plans are formulated aiming at the power failure risk level, and the loss under various power failure conditions of the key district is reduced to the maximum extent through the analysis, early warning and planning of the power failure event.
Description
Technical Field
The invention relates to the technical field of power monitoring analysis, in particular to a power failure monitoring analysis and early warning system and method for a key district.
Background
With the rapid development of energy technology, electric energy occupies an increasingly important position in the life of people, and higher requirements are put on the power supply capacity of power supply enterprises and the quality of service provided by people.
However, in the process of production, construction and transformation of a power supply enterprise, a fault power failure or power failure operation inevitably occurs, and normal production and life of power users are affected.
The key cell is one of key service objects of the power grid enterprise, and once a power failure event occurs to a customer of the key cell, the customer can cause larger economic loss and social influence, and the power failure hidden trouble of important users can be brought to various construction and maintenance of the power grid enterprise and power distribution network faults;
however, the main means of the power failure risk early warning of the power grid enterprise aiming at the key communities at present are as follows: the clients depending on the key cells report to the power grid enterprise client service center or contact with the electricity inspector to perform manual passive processing, so that the failure monitoring analysis of the key cells can lead to untimely power failure early warning of the key cells, thereby causing the users of the key cells to suffer serious economic loss and social influence.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a power failure monitoring analysis and early warning system and method for a key cell.
The first aspect provides a power outage monitoring analysis and early warning system for a key cell, which comprises a database server, a key cell analysis and identification module, a power outage data acquisition module, a power outage event analysis module, a power outage risk monitoring module, a power outage early warning module and an emergency plan making module;
the key cell analysis and identification module is used for calling the database server to extract the user address and the cell information, collecting the address information in the user file information, analyzing the key cells and the levels of each key city to form a key cell index, further associating the key cell to which the address belongs by matching the extracted user address with the index, and identifying the key cell information influenced by the power failure according to the power failure correlation analysis of the key cell;
the power failure data acquisition module is used for acquiring and managing various power failure event basic data and characteristic index data of the key cell;
the power outage event analysis module is used for acquiring the data of the power outage data acquisition module, constructing a corresponding power outage event analysis model according to the acquired data, and acquiring a corresponding power outage event analysis result according to the power outage event analysis model;
the power failure risk monitoring module is used for monitoring various power failure events analyzed by the power failure event analysis module in real time;
the power failure early warning module is used for carrying out power failure early warning according to the analysis result of the power failure event;
and the emergency plan making module is used for constructing a corresponding emergency plan according to the risk level of the power failure early warning.
In some embodiments, the key cell analysis and identification module comprises a key cell information management unit, a key cell and station area relation management unit, a key cell power failure correlation analysis unit and an algorithm matching and identification unit;
the key cell information management unit is used for constructing a 4-level address information base to form a key cell information base comprising provinces,
Setting a key cell name, automatically generating and storing a key cell code, establishing an index according to the key cell name and the key cell code, and acquiring a relation between the key cell and a station area;
the key cell and station area relation management unit is used for realizing key cell hierarchical management according to the key cell information management result;
the power failure correlation analysis unit of the key cell is used for constructing a power failure correlation analysis function of the key cell, analyzing the power failure correlation of the key cell by using the power failure information and the key cell information, establishing the association relationship between the power failure information and the key cell according to the power failure correlation analysis result of the key cell, and acquiring the user information influenced by the power failure
The algorithm matching and identifying unit is used for matching the information of the key cell to which the user belongs according to the processing results of the key cell information management unit and the key cell and station area relation management unit and combining with a BM25 algorithm, and identifying the information of the key cell to which the power failure affects according to the information of the user to which the power failure affects and the information of the key cell to which the user belongs.
In some embodiments, the outage data collection module comprises a base data analysis unit and a characteristic index data management unit;
the basic data analysis unit is used for collecting fault outage, outage associated household information, active emergency repair data, 95598 repair work orders and suspected outage information in the database server, realizing the acquisition of outage event characteristic data and providing a data basis for analyzing various outage events;
the characteristic index data management unit is used for analyzing data such as power outage information report quantity, user influence number, power outage duration, power outage report time and power outage time difference, customer repair demand quantity, power outage equipment quantity and the like according to unit, time and power outage type dimension based on the characteristic data acquisition results of various power outage events, and providing data indexes for identifying corresponding power outage events.
In some embodiments, the outage event analysis module includes a no-report outage event analysis unit, a high-risk outage event analysis unit, and a timeout outage event analysis unit;
the non-reported power outage event analysis unit is used for constructing a non-reported power outage event analysis model, accessing power outage information into the non-reported power outage event analysis model, establishing a time interval matching rule, carrying out information correlation matching analysis, and identifying non-reported power outage event information;
the high-risk power outage event analysis unit is used for carrying out public opinion high-risk power outage event analysis and identifying high-risk power outage events through comparison of analysis results;
the overtime power failure event analysis unit is used for constructing an overtime power failure event analysis model, carrying out risk analysis of power failure factors of different types such as fault power failure, planned power failure, temporary power failure and the like through the overtime power failure event analysis model, calculating power failure time lengths of different power failure events, and identifying the overtime power failure event through data analysis and comparison.
In some embodiments, the blackout risk monitoring module includes a non-reported blackout event monitoring unit, a high-risk blackout event monitoring unit, and a timeout blackout event risk monitoring unit;
the non-reporting power failure event monitoring unit is used for monitoring whether the non-reporting power failure event exists in the key cell in real time, and if the non-reporting power failure event exists, the message prompt is carried out;
the high-risk power failure event monitoring unit is used for monitoring whether a high-risk power failure event exists in a key cell in real time, and if the high-risk power failure event which is not reported exists, carrying out high-risk power failure event early warning message prompt;
the overtime power failure event risk monitoring unit is used for monitoring whether overtime power failure events exist in the key cells in real time, and if the overtime power failure events exist, carrying out overtime power failure event early warning message prompt.
In some embodiments, the power outage early warning module comprises a power outage early warning configuration unit, an early warning level maintenance unit and a power outage early warning information reporting unit;
the power failure early warning configuration unit is used for realizing power failure early warning message template configuration according to the corresponding power failure early warning message in the power failure risk monitoring module;
the early-warning level maintenance unit is used for carrying out power failure early-warning classification according to the power failure information monitored and identified by the power failure risk monitoring module and the configuration result of the power failure early-warning configuration unit, and generating power failure early-warning information;
and the power failure early warning information reporting unit is used for reporting the power failure early warning information.
In some embodiments, the emergency plan formulation module includes a plan formulation unit and a plan matching unit;
the plan making unit is used for making corresponding emergency plans based on the power failure early warning information under different early warning levels;
the plan matching unit is used for analyzing the characteristic attributes and the weights of various power failure events, then carrying out similarity calculation, and matching the corresponding emergency plans according to the calculation results.
In some embodiments, the emergency plan includes a blackout event pre-warning information unit, a pre-warning level, a pre-warning time, a blackout event affecting client quantity, a blackout duration, a blackout start time, a blackout end time, a blackout range, a blackout type, an emergency policy, an emergency plan formulation time, an emergency plan formulation personnel, an emergency plan formulation department, and an emergency plan formulation unit.
In some embodiments, an emergency plan auditing unit is further included for auditing the received emergency plan.
In a second aspect, the present application proposes a power outage monitoring analysis and early warning method for a key cell, including the following steps:
analyzing and identifying key cells;
collecting basic data and characteristic index data of various power failure events of the key cell;
monitoring various power failure events of the key cell in real time;
constructing a power outage event analysis model according to the collected and monitored data to obtain a corresponding power outage event analysis result;
performing power failure early warning according to the power failure event analysis result;
and constructing a corresponding emergency plan according to the risk level of the power failure early warning.
The invention has the beneficial effects that: the system identifies the key cell through analysis, and then collects basic data and characteristic index data of various power failure events of the key cell; in addition, various power failure events of the key cell are monitored in real time; the power failure early warning is carried out according to the data and the monitoring result, the traditional passive processing mode is abandoned, the power failure early warning is carried out for customers of the key district in time, the effects of reporting the power failure information in real time and accurately analyzing the power failure information to the home are achieved, corresponding emergency plans are formulated aiming at the power failure risk level, and the losses under various power failure conditions are reduced to the maximum extent through the analysis, early warning and planning of the power failure event, and the losses under various power failure conditions of the key district are reduced to the maximum extent.
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FIG. 1 is a system architecture diagram of the present invention.
Fig. 2 is a flow chart of the method of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In a first aspect, the present application proposes a power outage monitoring analysis and early warning system for a key cell, as shown in fig. 1, including a database server, a key cell analysis and identification module, a power outage data acquisition module, a power outage event analysis module, a power outage risk monitoring module, a power outage early warning module, and an emergency plan formulation module;
the key cell analysis and identification module is used for calling the database server to extract the user address and the cell information, collecting the address information in the user file information, analyzing the key cells and the levels of each key city to form a key cell index, further associating the key cell to which the address belongs by matching the extracted user address with the index, and identifying the key cell information influenced by the power failure according to the power failure correlation analysis of the key cell;
in some embodiments, the key cell analysis and identification module comprises a key cell information management unit, a key cell and station area relation management unit, a key cell power failure correlation analysis unit and an algorithm matching and identification unit;
the key cell information management unit is used for constructing a 4-level address information base to form a key cell information base comprising provinces,
Setting a key cell name, automatically generating and storing a key cell code, establishing an index according to the key cell name and the key cell code, and acquiring a relation between the key cell and a station area;
the key cell and station area relation management unit is used for realizing key cell hierarchical management according to the key cell information management result;
the method comprises the steps of dividing the level of an important cell according to the number of users in the important cell, dividing the level into an upper important cell, a middle important cell and a lower important cell, realizing the management of the association relation between an important cell file and the user of the important cell, realizing the management of the association relation between the important cell file and a station area according to the association relation between the important cell file and the user of the important cell, counting the number of covered clients of the important cell, and covering the range of the number of client groups according to the important cell;
the power outage correlation analysis unit is used for constructing a power outage correlation analysis function of the key cell, analyzing the power outage correlation of the power outage information and the key cell information, establishing an association relationship between the power outage information and the key cell according to the power outage correlation analysis result of the key cell, and acquiring user information influenced by the power outage;
and the information such as the power outage line, the power outage area, the power outage customer area and the like is extracted from the power outage information at intervals, the information such as the power outage line, the power outage area, the power outage customer area and the like is subjected to correlation analysis with the information such as the cell file, the cell power supply area and the like, and the subsequently newly generated power outage information of the key cell is subjected to superposition processing based on the association relation between the power outage information and the key cell, so that the new power outage user list and the new power outage area information of the key cell are superposed.
The algorithm matching and identifying unit is used for matching the information of the key cell to which the user belongs according to the processing results of the key cell information management unit and the key cell and station area relation management unit and combining with a BM25 algorithm, and identifying the information of the key cell to which the power failure affects according to the information of the user to which the power failure affects and the information of the key cell to which the user belongs.
Further, the BM25 algorithm adopted is:
wherein Q represents a query;
represents a free parameter, and the default value is 1.2; b represents a free parameter, default value of 0.75,
representing query terms->
Representing a key cell, wherein IDF represents reverse document frequency; />
Mean value of D is shown.
And substituting the full-network electricity user address into an address information index by using a natural language processing technology in combination with a BM25 algorithm, identifying the corresponding relation of the user number, the electricity address and the key cell, matching the key cell information of the user, and identifying the key cell information of the power failure according to the power failure influence user information and the key cell information of the user.
The power failure data acquisition module is used for acquiring and managing various power failure event basic data and characteristic index data of the key cell;
in some embodiments, the outage data collection module comprises a base data analysis unit and a characteristic index data management unit;
the basic data analysis unit is used for collecting fault outage, outage associated household information, active emergency repair data, 95598 repair work orders and suspected outage information in the database server, realizing the acquisition of outage event characteristic data and providing a data basis for analyzing various outage events;
the characteristic index data management unit is used for analyzing data such as power outage information report quantity, user influence number, power outage duration, power outage report time and power outage time difference, customer repair demand quantity, power outage equipment quantity and the like according to unit, time and power outage type dimension based on the characteristic data acquisition results of various power outage events, and providing data indexes for identifying corresponding power outage events.
The power outage event analysis module is used for acquiring the data of the power outage data acquisition module, constructing a corresponding power outage event analysis model according to the acquired data, and acquiring a corresponding power outage event analysis result according to the power outage event analysis model;
in some embodiments, the outage event analysis module includes a no-report outage event analysis unit, a high-risk outage event analysis unit, and a timeout outage event analysis unit;
the non-reported power outage event analysis unit is used for constructing a non-reported power outage event analysis model, accessing power outage information into the non-reported power outage event analysis model, establishing a time interval matching rule, carrying out information correlation matching analysis, and identifying non-reported power outage event information;
the steps of the built unreported power outage event analysis model comprise: sample data setting, feature engineering, model training, model self-learning, model tuning and model integration;
sample data setting: selecting data such as power outage report data, customer complaint work orders, internal supervision orders and the like in the history of 3 years, and setting model sample data;
characteristic engineering: analyzing the influence degree of each factor on the non-reported power failure event from the data of historical power failure reporting data, customer appeal work orders, internal supervision orders and the like, forming a characteristic index system, carrying out processing such as characteristic transformation, characteristic expansion and the like on selected characteristics, observing the distribution balance condition of the data, primarily analyzing the influence of the dimensional characteristics on whether risks exist or not, checking whether the dimensional characteristics have relevant characteristics or not, synthesizing the results of various methods and selecting proper characteristics for the model;
model training: automatically marking according to business rules by utilizing the data cleaned by the application system, forming a training set, and training the model;
model self-learning: and determining whether the sample with inconsistent model judgment with the actual situation belongs to the model classification error or not through the supervised model training result, and marking the sample as a misjudgment sample if the sample belongs to the model classification error. And reconstructing the training set by using the corrected erroneous judgment samples in a certain period as incremental data. Training an upgrade model according to the newly constructed data set;
and (3) model tuning: the model is optimized by selecting different data processing versions, different algorithm combinations and the like, the model effects of the different versions are calculated and evaluated, and the optimal algorithm is selected, wherein the algorithm comprises a naive Bayesian algorithm, a random forest and the like;
model integration: based on a model integration protocol designed and agreed in advance, a model integration unit is developed, and an unreported outage event analysis model is constructed.
Furthermore, based on the non-reported outage event analysis model, the network province reporting outage information access analysis is realized, and the 95598 fault reporting information and the suspected outage information access analysis are realized. And establishing a time interval matching rule, and carrying out information correlation matching analysis according to the time points of the outage information, the repair information and the suspected outage information to analyze the non-reported outage event information.
The high-risk power outage event analysis unit is used for carrying out public opinion high-risk power outage event analysis and identifying high-risk power outage events through comparison of analysis results;
further, the step of identifying a high risk outage event includes:
calculating the number of faults and power failures and complaint worksheets of each key cell in approximately 30 days, and storing the same
The number of faults and power failures, the mean value and standard deviation of the number of complaints and the work orders;
each key cell marks the average value of the number of fault stops per day and 1 time of standard deviation as a public opinion risk early warning threshold, marks the average value of the number of fault stops per day and 2 times of standard deviation as a public opinion high risk early warning threshold, and marks the average value of the number of complaints per day and 1 time of standard deviation as a public opinion risk early warning threshold, and marks the average value of the number of complaints per day and 2 times of standard deviation as a public opinion high risk early warning threshold;
and calculating whether the current-day fault outage number or the complaint work number of the key community exceeds a public opinion risk threshold, carrying out public opinion risk early warning when the current-day fault outage number or the complaint work number exceeds the public opinion risk early warning threshold, and identifying the current-day fault outage event as a high-risk power outage event when the current-day fault outage number or the complaint work number exceeds the public opinion risk early warning threshold.
The overtime power failure event analysis unit is used for constructing an overtime power failure event analysis model, carrying out risk analysis of power failure factors of different types such as fault power failure, planned power failure, temporary power failure and the like through the overtime power failure event analysis model, calculating power failure time lengths of different power failure events, and identifying the overtime power failure event through data analysis and comparison.
The method comprises the steps of constructing a overtime power failure event analysis model by adopting a data mining technology, training the overtime power failure event analysis model by utilizing overtime power failure event characteristics, wherein the overtime power failure event characteristics comprise power failure influence account number information and power failure time, performing data observation and data analysis based on the overtime power failure event analysis model, realizing correlation analysis of the power failure time and regional power failure requirement quantity influence, and analyzing different power failure time to cause a power failure correlation requirement quantity change relation. The model judges the power failure time length causing high complaint risk through analysis, supports the identification of overtime power failure events, carries out correlation analysis on the overtime time length of historical overtime power failure events and the customer complaint amount, uses a cluster analysis model to carry out feature clustering on each evaluation factor, and excavates the risk brought by overtime events.
The power failure risk monitoring module is used for monitoring various power failure events analyzed by the power failure event analysis module in real time;
in some embodiments, the blackout risk monitoring module includes a non-reported blackout event monitoring unit, a high-risk blackout event monitoring unit, and a timeout blackout event risk monitoring unit;
the non-reporting power failure event monitoring unit is used for monitoring whether the non-reporting power failure event exists in the key cell in real time, and if the non-reporting power failure event exists, the message prompt is carried out;
the high-risk power failure event monitoring unit is used for monitoring whether a high-risk power failure event exists in a key cell in real time, and if the high-risk power failure event which is not reported exists, carrying out high-risk power failure event early warning message prompt;
the monitoring types of the high-risk power failure event monitoring unit comprise:
1. monitoring whether the power outage event is a high-risk power outage event according to the class of the power outage influencing users, such as high-risk clients, power outage sensitive clients and the like, and if the class of the power outage influencing users contains the users with the appointed client class, carrying out high-risk power outage event early warning message prompt;
2. judging whether the power outage event is a high-risk power outage event according to whether a power outage influence user accords with a power outage sensitive customer power outage time analysis model, and if the power outage influence user accords with the power outage sensitive customer power outage time analysis model, carrying out high-risk power outage event early warning message prompt;
3. judging whether the power failure affecting user number is a high-risk power failure event or not according to the power failure affecting user number, and if the power failure affecting user number reaches a specified threshold, carrying out high-risk power failure event early warning message prompt.
The overtime power failure event risk monitoring unit is used for monitoring whether overtime power failure events exist in the key cells in real time, and if the overtime power failure events exist, carrying out overtime power failure event early warning message prompt.
The monitoring type of the overtime power failure event risk monitoring unit comprises:
1. based on a timeout power failure event analysis model, developing risk analysis of different types of power failure events such as fault power failure, planned power failure, temporary power failure and the like, calculating power failure time lengths of different power failure events, and identifying timeout power failure events through data analysis and comparison;
2. and calculating the actual power failure time length in real time according to the effective power failure information of the key cell, comparing the actual power failure time length with a power failure time length monitoring threshold, marking as a timeout power failure event if the actual power failure time length is larger than the power failure time length monitoring threshold, and prompting a timeout power failure event early warning message.
The power failure early warning module is used for carrying out power failure early warning according to the analysis result of the power failure event;
in some embodiments, the power outage early warning module comprises a power outage early warning configuration unit, an early warning level maintenance unit and a power outage early warning information reporting unit;
the power failure early warning configuration unit is used for realizing power failure early warning message template configuration according to the corresponding power failure early warning message in the power failure risk monitoring module;
wherein, the configuration of power failure early warning message template includes:
1. the maintenance should report the power failure event early warning message display content of not reporting, include: the power outage information generation system comprises a power outage province unit which shall report an un-reported power outage event, a city unit which shall report an un-reported power outage event, a message prompt generation time which shall report an un-reported power outage event, a power outage event number which shall report an un-reported power outage event, a message receiving department and a message receiving person; 2. maintaining high-risk power failure event early warning message display content, including: the system comprises a power outage event province unit, a high-risk power outage event city unit, a high-risk power outage event message prompt generation time, a high-risk power outage event early warning type, a high-risk power outage event influence user number, a message receiving department and a message receiving person; 3. the display content of the early warning message of the maintenance overtime power failure event comprises: the power failure time unit, the power failure event early warning generation time, the actual power failure time length, the time-out time length, the message receiving department and the message receiving personnel.
The early-warning level maintenance unit is used for carrying out power failure early-warning classification according to the power failure information monitored and identified by the power failure risk monitoring module and the configuration result of the power failure early-warning configuration unit, and generating power failure early-warning information;
and the power failure risk monitoring module is used for monitoring the identified risk power failure information and extracting relevant service parameters based on a power failure early warning template, and generating power failure early warning information for the power failure event identified by the power failure risk monitoring module, wherein the power failure risk monitoring module comprises a power failure event monitoring unit which is not reported and a high risk power failure event monitoring unit and a power failure event overtime risk monitoring unit.
Further, the corresponding power outage events are quantitatively analyzed according to the characteristics of various power outage events, a power outage event level identification rule is generated, and threshold values corresponding to various risk levels are set. Specifically, the larger the power outage influence account number range is, the higher the risk of a power outage event is, the smaller the power outage influence account number range is, the lower the risk of the power outage event is, for example, the power outage influence account number range is below 100 accounts or is limited in a small cell (namely, a lower-level key cell in key cell hierarchical management), the power outage event risk is considered to be low, and three-level power outage early warning is set;
when the number of users affected by the power outage is more than 1000 or limited in a street zone (namely, a middle-level key cell in the key cell hierarchical management), the power outage event risk is considered to be set as a secondary power outage early warning; when the number of users affected by the power outage is more than 5000 users or limited in a plurality of street areas (namely, the upper-level key cells in the key cell hierarchical management), the risk of the power outage event is considered to be high, and the power outage event is set as primary power outage early warning;
in addition, the longer the power outage time, the higher the risk of the power outage event, and the shorter the power outage time, the lower the risk of the power outage event; for example, if the power outage time is within one day, the risk of the power outage event is considered to be low, if the power outage time is more than one day and within three days, the risk of the power outage event is considered to be high, and if the power outage time is more than one week.
And the power failure early warning information reporting unit is used for reporting the power failure early warning information.
The method comprises the steps of 1, reporting the generated pre-warning information of the non-reported power failure event through pre-warning monitoring of the non-reported power failure event, and transmitting the pre-reported non-reported power failure event of different risk grades perceived in advance to customer service personnel of a corresponding key district, wherein the reporting content comprises the following steps: a power outage early warning information unit, power outage early warning information generation time, power outage early warning information content, power outage early warning information level, power outage early warning information type, power outage early warning information reporting time, power outage early warning information reporting personnel and the like;
2. through high-risk power failure event early warning monitoring, the high-risk power failure event early warning information that generates is reported, and the high-risk power failure event of different risk grades perceived in advance is issued to corresponding key district customer service personnel, and the report content includes: the power outage early warning information unit comprises power outage early warning information generation time, power outage early warning information content, power outage early warning information level, power outage early warning information type, power outage early warning information reporting time, power outage early warning information reporting personnel and the like.
3. Through the overtime power failure event early warning monitoring, the generated overtime power failure event early warning information is submitted, overtime power failure events with different risk grades perceived in advance are issued to corresponding key district customer service personnel, and the submitted content comprises: a power outage early warning information unit, power outage early warning information generation time, power outage early warning information content, power outage early warning information level, power outage early warning information type, power outage early warning information reporting time, power outage early warning information reporting personnel and the like;
and the emergency plan making module is used for constructing a corresponding emergency plan according to the risk level of the power failure early warning.
In some embodiments, the emergency plan formulation module includes a plan formulation unit and a plan matching unit;
the plan making unit is used for making corresponding emergency plans based on the power failure early warning information under different early warning levels;
and according to the power failure event early warning information of different early warning levels, different emergency plans are formulated, and a plan library is constructed.
The emergency plan comprises a power failure event early warning information unit, an early warning grade, early warning time, a power failure event influence client amount, a power failure duration, a power failure starting time, a power failure ending time, a power failure range, a power failure type, an emergency strategy, an emergency plan making time, an emergency plan making personnel, an emergency plan making department and an emergency plan making unit.
The plan matching unit is used for analyzing the characteristic attributes and the weights of various power failure events, then carrying out similarity calculation, and matching the corresponding emergency plans according to the calculation results.
The emergency plan is constructed in a way generally comprising twoThe method comprises the steps of early warning level and plan measure. As the pre-warning level and the pre-plan measure are in a corresponding relation, the corresponding relation can be matched with corresponding treatment measures rapidly. The early warning level can be regarded as an entity, the early warning level can be extracted into several key factors as the attributes of the level description, and the corresponding plan measures can also be regarded as an entity. And constructing a matching model of the emergency plan according to the matching model f, wherein f is A-B. Wherein A is a characteristic factor set of the level description, and B is a set of the plan measures. Based on the established plan library, for a given specific case C, if C exactly accords with one specific case of the plan library, the existing emergency plan is called, and if not, C= { A c ,B c }，A c ,B c A set of characteristic factors and a set of plan measures for a particular instance C, respectively.
Therefore, the specific execution flow of the plan matching unit includes:
inputting a power outage event
The method comprises the steps of carrying out a first treatment on the surface of the The outage event is expressed as: />
Wherein A is the characteristic attribute of the power failure event,
the weight value corresponding to the characteristic attribute is m, and the characteristic attribute number of the power failure event is m; the weight->
The value of (2) is +.>
The power failure event->
Weights of all feature attributes of (a)
Satisfy->
。
Further, we will have a power outage event to be matched
Expressed in the form of all the characteristic attributes contained therein and their corresponding weights. And, define the weight sum of all characteristic attribute in every power failure event as a constant value. In this embodiment, the constant value is l, and the weight corresponding to each feature attribute is +.>
The size of (c) may be given by the relevant expert or assigned by other means.
Will have a power failure event
And a plan library
Carrying out similarity calculation on the plans one by one;
plan library
Consists of a case C; each protocol C is expressed as +.>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein A is the characteristic attribute of the plan, B is the plan measure corresponding to the characteristic attribute, and n is the characteristic attribute number of the plan;
the similarity calculation formula is:
wherein,,
is a power failure event->
And->
The weight corresponding to the same characteristic attribute of a plan C, and k is the same characteristic attribute data number.
In this step, the power failure event
Characteristic properties and a library of plans>
The characteristic attributes of all the plans C are compared one by one, and the weights corresponding to the same characteristic attributes are accumulated, so that the power failure event +.>
The similarity S, k is less than or equal to min (m, n), S is less than or equal to 1, and the greater the S value is, the greater the similarity is, and vice versa is smaller.
Selecting a library of plans
Middle and the blackout event->
A plan with the maximum similarity S as the power failure event
The matching of the emergency plan is completed.
In some embodiments, an emergency plan auditing unit is further included for auditing the received emergency plan.
If the matched emergency plan passes the examination, the emergency plan is effective, and related departments can execute operations according to the plan; if the matched emergency plan is not checked, the emergency plan is returned to an emergency plan making link, and the emergency plan is modified or the current emergency plan is terminated according to the check comments.
In a second aspect, the present application proposes a power outage monitoring analysis and early warning method for a key cell, as shown in fig. 2, including steps S1 to S6:
s1: analyzing and identifying key cells;
s2: collecting basic data and characteristic index data of various power failure events of the key cell;
s3: monitoring various power failure events of the key cell in real time;
s4: constructing a power outage event analysis model according to the collected and monitored data to obtain a corresponding power outage event analysis result;
s5: performing power failure early warning according to the power failure event analysis result;
s6: and constructing a corresponding emergency plan according to the risk level of the power failure early warning.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and improvements made by those skilled in the art without departing from the present technical solution shall be considered as falling within the scope of the claims.
Claims (10)
1. A power failure monitoring analysis and early warning system for key district, its characterized in that: the system comprises a database server, a key cell analysis and identification module, a power outage data acquisition module, a power outage event analysis module, a power outage risk monitoring module, a power outage early warning module and an emergency plan making module;
the key cell analysis and identification module is used for calling the database server to extract the user address and the cell information, collecting the address information in the user file information, analyzing the key cells and the levels of each key city to form a key cell index, further associating the key cell to which the address belongs by matching the extracted user address with the index, and identifying the key cell information influenced by the power failure according to the power failure correlation analysis of the key cell;
the power failure data acquisition module is used for acquiring and managing various power failure event basic data and characteristic index data of the key cell;
the power outage event analysis module is used for acquiring the data of the power outage data acquisition module, constructing a corresponding power outage event analysis model according to the acquired data, and acquiring a corresponding power outage event analysis result according to the power outage event analysis model;
the power failure risk monitoring module is used for monitoring various power failure events analyzed by the power failure event analysis module in real time;
the power failure early warning module is used for carrying out power failure early warning according to the analysis result of the power failure event;
and the emergency plan making module is used for constructing a corresponding emergency plan according to the risk level of the power failure early warning.
2. The system according to claim 1, wherein: the key cell analysis and identification module comprises a key cell information management unit, a key cell and station area relation management unit, a key cell power failure correlation analysis unit and an algorithm matching and identification unit;
the key cell information management unit is used for constructing a 4-level address information base to form a key cell information base comprising provinces,
Setting a key cell name, automatically generating and storing a key cell code, establishing an index according to the key cell name and the key cell code, and acquiring a relation between the key cell and a station area;
the key cell and station area relation management unit is used for realizing key cell hierarchical management according to the key cell information management result;
the power outage correlation analysis unit is used for constructing a power outage correlation analysis function of the key cell, analyzing the power outage correlation of the power outage information and the key cell information, establishing an association relationship between the power outage information and the key cell according to the power outage correlation analysis result of the key cell, and acquiring user information influenced by the power outage;
the algorithm matching and identifying unit is used for matching the information of the key cell to which the user belongs according to the processing results of the key cell information management unit and the key cell and station area relation management unit and combining with a BM25 algorithm, and identifying the information of the key cell to which the power failure affects according to the information of the user to which the power failure affects and the information of the key cell to which the user belongs.
3. The system according to claim 2, wherein: the power failure data acquisition module comprises a basic data analysis unit and a characteristic index data management unit;
the basic data analysis unit is used for collecting fault outage, outage associated household information, active emergency repair data, 95598 repair work orders and suspected outage information in the database server, realizing the acquisition of outage event characteristic data and providing a data basis for analyzing various outage events;
the characteristic index data management unit is used for analyzing data such as power outage information report quantity, user influence number, power outage duration, power outage report time and power outage time difference, customer repair demand quantity, power outage equipment quantity and the like according to unit, time and power outage type dimension based on the characteristic data acquisition results of various power outage events, and providing data indexes for identifying corresponding power outage events.
4. A system according to claim 3, characterized in that: the power outage event analysis module comprises a power outage event analysis unit which is not reported and is required to report, a high-risk power outage event analysis unit and a timeout power outage event analysis unit;
the non-reported power outage event analysis unit is used for constructing a non-reported power outage event analysis model, accessing power outage information into the non-reported power outage event analysis model, establishing a time interval matching rule, carrying out information correlation matching analysis, and identifying non-reported power outage event information;
the high-risk power outage event analysis unit is used for carrying out public opinion high-risk power outage event analysis and identifying high-risk power outage events through comparison of analysis results;
the overtime power failure event analysis unit is used for constructing an overtime power failure event analysis model, carrying out risk analysis of power failure factors of different types such as fault power failure, planned power failure, temporary power failure and the like through the overtime power failure event analysis model, calculating power failure time lengths of different power failure events, and identifying the overtime power failure event through data analysis and comparison.
5. The system according to claim 4, wherein: the power failure risk monitoring module comprises a power failure event monitoring unit which is not reported and is required to report, a high-risk power failure event monitoring unit and a overtime power failure event risk monitoring unit;
the non-reporting power failure event monitoring unit is used for monitoring whether the non-reporting power failure event exists in the key cell in real time, and if the non-reporting power failure event exists, the message prompt is carried out;
the high-risk power failure event monitoring unit is used for monitoring whether a high-risk power failure event exists in a key cell in real time, and if the high-risk power failure event which is not reported exists, carrying out high-risk power failure event early warning message prompt;
the overtime power failure event risk monitoring unit is used for monitoring whether overtime power failure events exist in the key cells in real time, and if the overtime power failure events exist, carrying out overtime power failure event early warning message prompt.
6. The system according to claim 5, wherein: the power failure early warning module comprises a power failure early warning configuration unit, an early warning level maintenance unit and a power failure early warning information reporting unit;
the power failure early warning configuration unit is used for realizing power failure early warning message template configuration according to the corresponding power failure early warning message in the power failure risk monitoring module;
the early-warning level maintenance unit is used for carrying out power failure early-warning classification according to the power failure information monitored and identified by the power failure risk monitoring module and the configuration result of the power failure early-warning configuration unit, and generating power failure early-warning information;
and the power failure early warning information reporting unit is used for reporting the power failure early warning information.
7. The system according to claim 6, wherein: the emergency plan making module comprises a plan making unit and a plan matching unit;
the plan making unit is used for making corresponding emergency plans based on the power failure early warning information under different early warning levels;
the plan matching unit is used for analyzing the characteristic attributes and the weights of various power failure events, then carrying out similarity calculation, and matching the corresponding emergency plans according to the calculation results.
8. The system according to claim 7, wherein: the emergency plan comprises a power failure event early warning information unit, an early warning grade, early warning time, a power failure event influence client quantity, a power failure duration, a power failure starting time, a power failure ending time, a power failure range, a power failure type, an emergency strategy, an emergency plan making time, an emergency plan making personnel, an emergency plan making department and an emergency plan making unit.
9. The system according to claim 8, wherein: the system further comprises an emergency plan auditing unit, wherein the emergency plan auditing unit is used for auditing the received emergency plan.
10. A power failure monitoring analysis and early warning method for key cells is characterized in that: the method comprises the following steps:
analyzing and identifying key cells;
collecting basic data and characteristic index data of various power failure events of the key cell;
monitoring various power failure events of the key cell in real time;
constructing a power outage event analysis model according to the collected and monitored data to obtain a corresponding power outage event analysis result;
performing power failure early warning according to the power failure event analysis result;
and constructing a corresponding emergency plan according to the risk level of the power failure early warning.
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