CN111080142B - Active service auxiliary judgment method based on power failure reporting - Google Patents

Active service auxiliary judgment method based on power failure reporting Download PDF

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CN111080142B
CN111080142B CN201911320069.1A CN201911320069A CN111080142B CN 111080142 B CN111080142 B CN 111080142B CN 201911320069 A CN201911320069 A CN 201911320069A CN 111080142 B CN111080142 B CN 111080142B
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user
fault
priority
parameter
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CN111080142A (en
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张志生
高尚飞
付俊
赵悦明
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Information Center of Yunnan Power Grid Co Ltd
Kunming Enersun Technology Co Ltd
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Information Center of Yunnan Power Grid Co Ltd
Kunming Enersun Technology Co Ltd
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    • 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
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • 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 an active service auxiliary judgment method based on power failure reporting, which comprises the following steps: a. analyzing the dimensionality influencing the user priority ranking; b. capturing historical behavior data of a user; c. establishing a user priority ranking model; d. and the user priority evaluation is realized through the historical behavior data of the user and the user priority ranking model. The invention is based on the user priority ranking algorithm of the power failure fault reporting behavior, evaluates the user priority by analyzing the historical power failure fault reporting behavior of the user, preferentially solves the problem of power failure of the user who really has a fault and is urgent, improves the user experience and improves the service quality of the power grid.

Description

Active service auxiliary judgment method based on power failure reporting
Technical Field
The invention belongs to the technical field of electric power data analysis, and particularly relates to an active service auxiliary judgment method based on electric power fault reporting.
Background
Along with the development of the internet technology in the power industry, more and more convenient power systems are applied on line, and for example, a distribution network emergency repair system can distribute orders for users to repair when the users declare power failure. However, when a user reports a power failure fault, the original network repair system is arranged according to the order of the user access telephone, and a large amount of human resources are wasted under the conditions of partial false reports, random reports and the like, so that the problems of overlong waiting time, excessive complaints, poor user experience and the like of partial users are caused.
Disclosure of Invention
Based on the defects of the prior art, the invention provides an active service auxiliary judgment method based on power failure reporting.
The invention is realized by the following technical scheme.
An active service auxiliary judgment method based on power failure reporting comprises the following steps:
a. analyzing the dimensionality influencing the user priority ranking;
b. capturing historical behavior data of a user;
c. establishing a user priority ranking model;
d. and the user priority evaluation is realized through the historical behavior data of the user and the user priority ranking model.
Preferably, the dimension analysis influencing the user prioritization in the step a is as follows:
a1, influence of the user repair fault accuracy on the user priority;
a2, influence of user complaint conditions on user priority;
a3, influence of user false alarm on user priority;
a4, influence of the number of incoming calls of the user on the priority for the same fault.
Preferably, the historical behavior data of the user to be captured in step b is as follows:
b1, taking the year as a unit, capturing behavior data of a user reporting the power failure fault within one year of history;
b2, capturing user declaration fault data;
b3, capturing data confirmed as the fault in the user declaration fault;
b4, capturing the total number of the fault calls declared by the user;
b5, capturing the number of complaints of the user;
b6, capturing the false alarm fault number of the user;
b7, grasping the number of times of incoming calls for the fault declaration by the user.
Preferably, the dimension of the user prioritization model in step c is as follows:
c1, the accuracy of the fault reported by the user;
c2, reporting the fault incoming call frequency by the user;
c3, probability of user complaints;
c4, probability of false alarm of user.
Preferably, the user priority ranking model is established by using the accuracy of the fault reported by the user, the incoming call frequency of the fault reported by the user, the probability of the complaint of the user and the probability of the false report of the user, and the method specifically comprises the following steps:
failure accuracy:
x＝∑t∈1yearAt/∑t∈1vearBt
incoming call frequency of fault reported by user:
y＝(∑t∈1yearTt/∑t∈1yearAt)2
the false alarm probability of the user is as follows:
Figure BDA0002326899030000021
probability of customer complaints:
P(C)＝(Σt∈1yearCt/Σt∈1yearTt)·E
wherein, the coefficient A is the total number of reported faults, the coefficient B is the number of real faults confirmed in the reported faults, the coefficient T is the total number of reported fault calls, the coefficient C is the number of complaints, the coefficient D is the number of false-reported faults, the coefficient E is the number of calls aiming at the fault, and Sigma ist∈1yearρtRepresenting that one year is taken as statistical time, wherein t represents time, and t belongs to 1year to represent that the statistical time is one year; according to the habit of the user, the priority ranking model determines two parameters, wherein the parameter 1 is F (n):
F(n)＝x+y+z，
F(n)＝∑t∈1year(A3+BT2+ABD2)t/∑t∈1year(BA2)t，
the values of T and D have greater influence on the parameter F (n), namely the more the historical false alarm number is, the more the same fault is frequently reported and repaired, and the lower the priority of the user is ranked;
the parameter 2 is the user complaint probability P (C).
Preferably, two parameters f (n) and p (c) are calculated according to the established user prioritization model, and the user prioritization is determined by using the two parameters, which are as follows:
if the parameter F (n) is less than or equal to 5, the user priority is rated as A level;
if the parameter is 5 < F (n) is less than or equal to 10, the user priority is ranked as B level;
if the parameter is 10 < F (n) is less than or equal to 15, the user priority is ranked as C;
if the parameter 15 is less than F (n), the user priority is ranked as D;
parameter P (C) as a second criterion for user priority rating, user rating being determined by the result of parameter F (n) when parameter P (C) < 1; when the parameter P (C) is more than or equal to 1, the user is directly graded into A grade.
The invention is based on the user priority ranking algorithm of the power failure fault reporting behavior, evaluates the user priority by analyzing the historical power failure fault reporting behavior of the user, preferentially solves the problem of power failure of the user who really has a fault and is urgent, improves the user experience and improves the service quality of the power grid.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a logic analysis diagram of the present invention.
Detailed Description
The present invention is further described with reference to fig. 1 and 2 below:
as shown in fig. 1, an active service auxiliary determination method based on power failure reporting includes: the method comprises the following steps:
a. analyzing the dimensionality influencing the user priority ordering;
b. capturing historical behavior data of a user;
c. establishing a user priority ranking model;
d. and the user priority evaluation is realized through the historical behavior data of the user and the user priority ranking model.
The dimensionality influencing the user priority ordering in the step a is divided into the accuracy of the fault reported by the user, the false report condition of the user, the complaint condition of the user and the incoming call frequency of the same fault; the priority of the user can be reduced when the user has more false alarms; the incoming call frequency is high for the same fault, the incoming call repeatedly occupies the electric channel, the priority of the user can be reduced, the accuracy rate of the fault reported by the user is high, and the priority of the user can be improved. If the user has a complaint tendency, the user fault needs to be treated as soon as possible before the complaint of the user, so that the priority of the user is improved to be optimal when the user has the complaint tendency.
As shown in fig. 2, historical behavior data of the number of faults to be captured and reported, the number of confirmed faults, the number of fault calls to report, the number of complaints, the number of power failure false reports, and the number of power failure calls of this time are analyzed according to the dimension affecting the priority ranking of users, and as shown in table 1, historical fault repair data of four users are captured:
TABLE 1
Figure BDA0002326899030000051
Modeling was performed using the base data, resulting in the following model:
failure accuracy:
x＝∑t∈1yearAt/∑t∈1yearBt
incoming call frequency of fault reported by user:
y＝(∑t∈1yearTt/∑t∈1yearAt)2
the false alarm probability of the user is as follows:
Figure BDA0002326899030000052
probability of customer complaints:
P(C)＝(Σt∈1yearCt/Σt∈1yearTt)·E
wherein, the coefficient A is the total number of reported faults, the coefficient B is the number of confirmed real faults in the reported faults, the coefficient T is the total number of reported fault incoming calls, the coefficient C is the number of complaints, the coefficient D is the number of false-reported faults, the coefficient E is the number of incoming calls for the fault, and sigmat∈1yearρtThe expression is represented by taking one year as the statistical time, wherein t represents the time, and t epsilon 1year represents the statistical time as one year.
According to the habit of the user, the priority ranking model determines two parameters, wherein the parameter 1 is F (n):
F(n)＝x+y+z，
F(n)＝∑t∈1year(A3+BT2+ABD2)t/∑t∈1year(BA2)t，
the values of T and D have greater influence on the parameter F (n), namely the more the historical false alarm number is, the more the same fault is frequently reported and repaired, and the lower the priority of the user is ranked;
the parameter 2 is the user complaint probability P (C).
According to the obtained user data, calculating to obtain a user priority score and a complaint probability, as shown in table 2:
TABLE 2
Figure BDA0002326899030000061
Two parameters F (n) and P (C) are obtained by model calculation to judge the user priority ranking, which is as follows:
if the parameter F (n) is less than or equal to 5, the user priority is rated as A level;
if the parameter is 5 < F (n) is less than or equal to 10, the user priority is ranked as B level;
if the parameter is 10 < F (n) is less than or equal to 15, the user priority is ranked as C;
if the parameter 15 is less than F (n), the user priority is ranked as D;
parameter P (C) as a second criterion for user priority rating, where the user rating is determined by the result of parameter F (n) when parameter P (C) < 1, and where parameter P (C) ≧ 1, the user is directly rated as level A.
As shown in Table 2, the user 2 has a priority rating of more than 5 and should be rated as class B, but has a complaint tendency because the complaint probability is more than 1, and is rated as the optimal class A.
The above disclosure is not intended to limit the scope of the invention, which is defined by the claims, but is intended to cover all modifications within the scope and spirit of the invention.

Claims (3)

1. An active service auxiliary judgment method based on power failure reporting is characterized by comprising the following steps:
a. analyzing the dimensionality influencing the user priority ranking;
b. capturing historical behavior data of a user;
c. establishing a user priority ranking model;
d. the user priority evaluation is realized through the historical behavior data of the user and the user priority sorting model;
the dimensionality of the user prioritization model in step c is as follows:
c1, the accuracy of the fault reported by the user;
c2, reporting the fault incoming call frequency by the user;
c3, probability of user complaints;
c4, probability of false alarm of the user;
establishing a user priority ranking model by using the accuracy of the fault reported by the user, the incoming call frequency of the fault reported by the user, the probability of complaint of the user and the probability of false report of the user, wherein the user priority ranking model specifically comprises the following steps:
failure accuracy:
x＝Σt∈1yearAt/Σt∈1yearBt
incoming call frequency of fault reported by user:
y＝(∑t∈1yearTt/∑t∈1yearAt)2
the false alarm probability of the user is as follows:
Figure FDA0003536595890000011
probability of customer complaints:
P(C)＝(Σt∈1yearCt/Σt∈1yearTt)·E
wherein, the coefficient A is the total number of reported faults, the coefficient B is the number of real faults confirmed in the reported faults, the coefficient T is the total number of reported fault calls, the coefficient C is the number of complaints, the coefficient D is the number of false-reported faults, the coefficient E is the number of calls aiming at the fault, and Sigma ist∈1yearρtRepresenting that one year is taken as statistical time, wherein t represents time, and t epsilon 1year represents that the statistical time is one year; according to the habit of the user, the priority ranking model determines two parameters, wherein the parameter 1 is F (n):
F(n)＝x+y+z，
F(n)＝∑t∈1year(A3+BT2+ABD2)t/∑t∈1year(BA2)t，
the values of T and D have greater influence on the parameter F (n), namely the more the historical false alarm number is, the more the same fault is frequently reported and repaired, and the lower the priority of the user is ranked;
parameter 2 is the probability of complaint of the user P (C);
according to the established user priority ranking model, two parameters F (n) and P (C) are calculated, and the user priority ranking is judged by using the two parameters, which are specifically as follows:
if the parameter F (n) is less than or equal to 5, the user priority is rated as A level;
if the parameter is 5 < F (n) is less than or equal to 10, the user priority is ranked as B level;
if the parameter is 10 < F (n) ≦ 15, the user priority is ranked as C;
if the parameter 15 is less than F (n), the user priority is ranked as D;
parameter P (C) as a second criterion for user priority rating, user rating being determined by the result of parameter F (n) when parameter P (C) < 1; when the parameter P (C) is more than or equal to 1, the user is directly graded into A grade.
2. The active service auxiliary decision method based on power failure reporting as claimed in claim 1, wherein: the dimension analysis influencing the user prioritization in the step a is as follows:
a1, influence of the user repair fault accuracy on the user priority;
a2, influence of user complaint conditions on user priority;
a3, influence of user false alarm on user priority;
a4, influence of the number of incoming calls of the user on the priority for the same fault.
3. The active service auxiliary decision method based on power failure reporting as claimed in claim 1, wherein: the historical behavior data of the user required to be captured in the step b is as follows:
b1, taking the year as a unit, capturing behavior data of a user reporting the power failure fault within one year of history;
b2, capturing user declaration fault data;
b3, capturing data confirmed as the fault in the user declaration fault;
b4, capturing the total number of the fault calls declared by the user;
b5, capturing the number of complaints of the user;
b6, capturing the false alarm fault number of the user;
b7, grasping the number of times of incoming calls for the fault declaration by the user.
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