CN111325475A - Emergency repair work order evaluation factor analysis method based on total log-likelihood algorithm - Google Patents

Emergency repair work order evaluation factor analysis method based on total log-likelihood algorithm Download PDF

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CN111325475A
CN111325475A CN202010142596.4A CN202010142596A CN111325475A CN 111325475 A CN111325475 A CN 111325475A CN 202010142596 A CN202010142596 A CN 202010142596A CN 111325475 A CN111325475 A CN 111325475A
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赵越
刘江东
杨川
濮实
徐力
汪波
缪凯
于航
于鹏飞
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Xiamen Epgis Information Technology Co ltd
State Grid Jiangsu Electric Power Co ltd Yangzhou Power Supply Branch
State Grid Jiangsu Electric Power Co Ltd
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State Grid Jiangsu Electric Power Co ltd Yangzhou Power Supply Branch
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Abstract

Provided is a rush-repair work order evaluation factor analysis method based on a total log likelihood algorithm. The method relates to the field of power supply, in particular to a rush-repair work order evaluation factor analysis method based on a total log likelihood algorithm. The method for analyzing the evaluation factors of the emergency repair work orders based on the total log likelihood algorithm can analyze and predict user evaluation according to intermediate data in the repair process. The invention adopts the maximum log-likelihood algorithm, can carry out regression analysis on the data of inventory and increment service production repair report through machine learning to obtain the real relationship between the user evaluation of the repair work order and each link data, can calculate the evaluation and predict the evaluation, is convenient to improve the service quality, and further improves the user satisfaction.

Description

Emergency repair work order evaluation factor analysis method based on total log-likelihood algorithm
Technical Field
The invention relates to the field of power supply, in particular to a rush-repair work order evaluation factor analysis method based on a total log likelihood algorithm.
Background
The power supply service command center mainly relates to a more important closed flow loop about telephone repair, and the flow links are as follows: the method comprises the steps of user telephone repair, failure study and judgment of a virtual Ai commander, path planning analysis, automatic dispatching to an optimal emergency repair team, machine or manual acquisition of user evaluation on current emergency repair or service. The service is used as a service department, the final purpose is to improve the satisfaction degree of users, and the evaluation of the satisfaction degree is over 90 percent.
Although the flow can realize intelligent order receiving, analysis and work order distribution through artificial intelligent voice recognition, program customization development and algorithm design, and a large amount of multidimensional process data and final user evaluation in each link are stored; but the linear relation between various process data in each link and the final user evaluation cannot be known exactly.
The existing evaluation analysis adopts customized formula analysis, one formula is applied to all data for service and repair, a machine learning algorithm is not adopted, evaluation cannot be predicted, only an optimal allocation strategy can be calculated, and a client cannot be guaranteed to have good evaluation on the allocation strategy.
Disclosure of Invention
Aiming at the problems, the invention provides the emergency repair work order evaluation factor analysis method based on the total log likelihood algorithm, which can analyze and predict the user evaluation according to the intermediate data in the repair reporting process.
The technical scheme of the invention is as follows: the method comprises the following steps:
s1, selecting the last year as a sample period, and collecting intermediate data related to user evaluation of each link in the repair flow as a repair sample;
s2, performing regression analysis by adopting a maximum likelihood estimation method to obtain a linear relation between all intermediate data in each repair sample and final evaluation of the user;
and S3, analyzing and predicting user evaluation according to the linear relation.
In step S1, links in the repair reporting flow include user telephone repair reporting, artificial intelligence research and judgment and work order distribution;
the repair variables in the repair report of the user telephone comprise a user mobile phone, a user number, a power failure type, a power failure cell and a power failure address;
the repair variables in the artificial intelligence research and judgment comprise a thoroughly-copied result, a recall result, a dispatch analysis result and a dispatch analysis time;
repair variables in the work order distribution include team information, personnel information, the number of work orders to be handled, arrival time, historical repair quality, repair duration, repair area, repair force configuration, vehicle configuration and repair spare parts.
The step S2 includes the following steps:
s2.1, the relationship between the reported correction variable and the error in each sample is as follows: true user rating-predicted user rating + error
y(i)＝θTx(i)+ε(i)
y(i)Represents the true user rating, θ, of the ith sampleTMatrix transposition, x, representing the true value of the ith sample and the prediction evaluation(i)Represents the repair variable, θ, for the ith sampleTx(i)Denotes the predicted value of the ith sample, ∈(i)Representing the error of the ith real value and the ith predicted value;
s2.2, probability Density function substitution
The probability density function is formulated as:
Figure BDA0002399603380000021
(x-. mu.) represents the error ε in step S2.1(i)Wherein μ and σ are mean and variance, respectively, and substituting them into the formula in step S2.1, the probability density function substitution process for the ith sample is as follows:
Figure BDA0002399603380000022
p(ε(i))
the deformation is as follows:
Figure BDA0002399603380000023
the total likelihood represents the likelihood multiplication of each sample, the following formula represents the m rows of samples, and the total likelihood of the i intermediate variables is:
Figure BDA0002399603380000031
the total likelihood formula is logarithmized as follows:
Figure BDA0002399603380000032
l (theta) represents the maximum total likelihood, deduces the need
Figure BDA0002399603380000033
The fraction is minimal;
namely, it is
Figure BDA0002399603380000034
J (theta) is a total log likelihood algorithm, m rows and i columns of repair sample repair variables are sequentially substituted into the formula to obtain the minimum total log likelihood, hθNamely a linear relation model of the report variables and the final evaluation of the user.
The invention adopts the maximum log-likelihood algorithm, can carry out regression analysis on the data of inventory and increment service production repair report through machine learning to obtain the real relationship between the user evaluation of the repair work order and each link data, can calculate the evaluation and predict the evaluation, is convenient to improve the service quality, and further improves the user satisfaction.
Detailed Description
The invention comprises the following steps:
s1, selecting the last year as a sample period, and collecting intermediate data related to user evaluation of each link in the repair flow as a repair sample;
s2, performing regression analysis by adopting a maximum likelihood estimation method to obtain a linear relation between all intermediate data in each repair sample and final evaluation of the user;
and S3, analyzing and predicting user evaluation according to the linear relation.
In step S1, as shown in table 1, the links in the repair reporting process include user telephone repair reporting, artificial intelligence research and judgment, and work order distribution;
the repair variables in the repair report of the user telephone comprise a user mobile phone, a user number, a power failure type, a power failure cell and a power failure address;
the repair variables in the artificial intelligence research and judgment comprise a thoroughly-copied result, a recall result, a dispatch analysis result and a dispatch analysis time;
repair variables in the work order distribution include team information, personnel information, the number of work orders to be handled, arrival time, historical repair quality, repair duration, repair area, repair force configuration, vehicle configuration and repair spare parts.
Figure BDA0002399603380000041
TABLE 1
In step S2, the maximum likelihood estimation is a statistical method, which is used to solve the parameters of the related probability density function of the sample set, and it can be obtained from the mathematical proof that the total likelihood of all the repair sample sets is maximum, which indicates that the sample set is more in line with the positive distribution, and the fitting is the most sufficient, and the obtained repair variables and the final user evaluation relationship model are in the most optimal linear function relationship.
According to equivalent substitution: the total likelihood is maximum and is approximately equal to the multiplication of the likelihood of each sample and the maximum time is approximately equal to the multiplication of the probability of each sample and the maximum time is approximately equal to the optimal solution
The step S2 includes the following steps:
s2.1, the relationship between the reported correction variable and the error in each sample is as follows:
y(i)＝θTx(i)+ε(i)
y(i)represents the true user rating, θ, of the ith sampleTMatrix transposition, x, representing the true value of the ith sample and the prediction evaluation(i)Represents the repair variable, θ, for the ith sampleTx(i)Denotes the predicted value of the ith sample, ∈(i)Representing the error of the ith real value and the ith predicted value;
s2.2, substituting the probability density function:
the probability density function is formulated as:
Figure BDA0002399603380000051
(x-. mu.) represents the error ε in step S2.1(i)Wherein μ and σ are mean and variance, respectively, and substituting them into the formula in step S2.1, the probability density function substitution process for the ith sample is as follows:
Figure BDA0002399603380000052
p(ε(i))
the deformation is as follows:
Figure BDA0002399603380000053
the total likelihood represents the likelihood multiplication of each sample, the following formula represents the m rows of samples, and the total likelihood of the i intermediate variables is:
Figure BDA0002399603380000061
because the formula relates to a large number of multiplication iterations, the solving speed of a computer is low, and more resources are occupied; the multiplication iteration operation is changed into addition iteration according to the following formula, so that the solving speed is greatly improved, and the resource occupation is reduced.
The total likelihood formula is logarithmized as follows:
Figure BDA0002399603380000062
l (theta) represents the maximum total likelihood, deduces the need
Figure BDA0002399603380000063
The fraction is minimal;
namely, it is
Figure BDA0002399603380000064
J (theta) is a total log likelihood algorithm, m rows and i columns of repair sample repair variables are sequentially substituted into the formula to obtain the minimum total log likelihood, namely hθThe row vector in the repair reporting variable is the optimal solution;
hθnamely a linear relation model of the report variables and the final evaluation of the user.
Through hθThe function can know the weight of each factor evaluated by the customer of the total log likelihood emergency repair work order and can also be used according to hθBefore the client is not evaluated, the intermediate variables of the substitution process are carried out at any timeAnd (4) predicting an evaluation result, and avoiding low evaluation of a user in the emergency repair command process.
According to the method, regression analysis of data is carried out by means of a machine learning algorithm, a relation model of process data and user evaluation is obtained, intermediate data with large weight is improved in a targeted mode, user evaluation is improved, user evaluation can be predicted in advance, a response can be made in time, and low-grade evaluation is avoided.

Claims (3)

1. A rush-repair work order evaluation factor analysis method based on a total log likelihood algorithm is characterized by comprising the following steps:
s1, selecting the last year as a sample period, and collecting intermediate data related to user evaluation of each link in the repair flow as a repair sample;
s2, performing regression analysis by adopting a maximum likelihood estimation method to obtain a linear relation between all intermediate data in each repair sample and final evaluation of the user;
and S3, analyzing and predicting user evaluation according to the linear relation.
2. The rush-repair work order evaluation factor analysis method based on the logarithm total likelihood algorithm as claimed in claim 1, wherein in step S1, links in the repair reporting process include user telephone repair reporting, artificial intelligence research and judgment, and work order distribution;
the repair variables in the repair report of the user telephone comprise a user mobile phone, a user number, a power failure type, a power failure cell and a power failure address;
the repair variables in the artificial intelligence research and judgment comprise a thoroughly-copied result, a recall result, a dispatch analysis result and a dispatch analysis time;
repair variables in the work order distribution include team information, personnel information, the number of work orders to be handled, arrival time, historical repair quality, repair duration, repair area, repair force configuration, vehicle configuration and repair spare parts.
3. The rush repair work order evaluation factor analysis method based on the log total likelihood algorithm as claimed in claim 1, wherein the step S2 comprises the following steps:
s2.1, the relationship between the reported correction variable and the error in each sample is as follows:
y(i)＝θTx(i)+ε(i)
y(i)represents the true user rating, θ, of the ith sampleTMatrix transposition, x, representing the true value of the ith sample and the prediction evaluation(i)Represents the repair variable, θ, for the ith sampleTx(i)Denotes the predicted value of the ith sample, ∈(i)Representing the error of the ith real value and the ith predicted value;
s2.2, substituting the probability density function:
the probability density function is formulated as:
Figure FDA0002399603370000011
(x-. mu.) represents the error ε in step S2.1(i)Wherein μ and σ are mean and variance, respectively, and substituting them into the formula in step S2.1, the probability density function substitution process for the ith sample is as follows:
Figure FDA0002399603370000021
p(∈(i))
the deformation is as follows:
Figure FDA0002399603370000022
the total likelihood represents the likelihood multiplication of each sample, the following formula represents the m rows of samples, and the total likelihood of the i intermediate variables is:
Figure FDA0002399603370000023
the total likelihood formula is logarithmized as follows:
Figure FDA0002399603370000024
l (theta) represents the maximum total likelihood, deduces the need
Figure FDA0002399603370000025
The fraction is minimal;
namely, it is
Figure FDA0002399603370000031
J (theta) is a total log likelihood algorithm, m rows and i columns of repair sample repair variables are sequentially substituted into the formula to obtain the minimum total log likelihood, hθNamely a linear relation model of the report variables and the final evaluation of the user.
CN202010142596.4A 2020-03-04 2020-03-04 Emergency repair work order evaluation factor analysis method based on total log-likelihood algorithm Pending CN111325475A (en)

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Application publication date: 20200623