CN114092147A - User holographic portrait label generation method based on energy power big data - Google Patents

User holographic portrait label generation method based on energy power big data Download PDF

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CN114092147A
CN114092147A CN202111391724.XA CN202111391724A CN114092147A CN 114092147 A CN114092147 A CN 114092147A CN 202111391724 A CN202111391724 A CN 202111391724A CN 114092147 A CN114092147 A CN 114092147A
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郑真
黄晨宏
马小丽
黄一楠
李建宁
颜华敏
杨国健
张颖
卢婧婧
蒋献伟
马晔晖
何之倬
牟锴
汪笃红
张冠花
蒋晨
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention discloses a user holographic portrait label generation method based on energy power big data, which is characterized in that a user characteristic label is generated by utilizing a big data processing technology, and the user characteristic label is generated by analyzing a characteristic appeal index of a user and utilizing a big data algorithm model; the basic database configuration of the big data processing technology is constructed around client appeal, opinions and consultation data streams of channels such as power 95598, power intranet extranet, power intranet, WeChat public line, business hall opinion book and the like are imported into the basic database to serve as original sources of label data, and the client is marked in a label mode through data analysis. The invention can integrate various source data of a company, build a multi-dimensional and three-dimensional customer portrait by relying on a big data analysis technology, and describe the deep level behavior characteristics of the customer through a label.

Description

User holographic portrait label generation method based on energy power big data
Technical Field
The invention relates to a user holographic portrait label generation method based on energy power big data, which is used in the field of power user management.
Background
The development of big data technology provides technical support for accurate identification of customers of power enterprises, and provides technical support for making targeted user service strategies, which is very critical for improving power user experience in the future. With the opening of the electricity selling side, an electricity selling market with participation of multiple parties is about to be formed, and a new test is brought to the traditional power supply enterprises. The brand impression of a customer on an electric power enterprise changes along with the change of roles and functions of the enterprise, the brand image is positioned at the intersection of monopolized state enterprises and service type public institutions, the power supply enterprise needs to actively attack, the customer demand under the new situation is mastered, the service upgrade is continuously promoted, and the comprehensive promotion of the service brand image is promoted. At present, the electric power enterprise has two problems in the knowledge of power consumers: firstly, lack complete electric power user's description, the user uses the user number as the main part in the marketing system, and the contact means is an attribute of user, and a user is a contact means generally, and uses the customer to dial 95598 phone as the main part in the 95598 system, and with the single user in the marketing system be many-to-one relation, therefore both can not realize accurate correspondence. And secondly, a method for classifying all-round information of the power users based on the whole life cycle of the power users such as basic attributes, social attributes, value attributes, service records and the like is lacked, and data support cannot be provided for a novel customer management mode.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a user holographic portrait label generation method based on energy power big data, which can realize power user classification management.
One technical scheme for achieving the above purpose is as follows: a user holographic portrait label generation method based on energy and power big data is characterized in that a user characteristic label is generated by utilizing a big data processing technology, the user characteristic label generates a database of characteristic appeal indexes by analyzing the characteristic appeal indexes of a user and utilizing a big data algorithm model; the basic database configuration of the big data processing technology is constructed around client appeal, opinions and consultation data streams of channels such as power 95598, power intranet extranet, power intranet, WeChat public line, business hall opinion book and the like are imported into the basic database to serve as original sources of label data, and the client is marked in a label mode through data analysis.
Further, a database of the characteristic appeal indexes is constructed, and the database comprises the following sub-database branches: the power grid construction appeal characteristic index sub-database, the power supply quality appeal characteristic index sub-database, the fault emergency repair appeal characteristic index sub-database, the business appeal characteristic index sub-database and the service appeal characteristic index sub-database.
Still further, the power grid construction appeal characteristic index sub-database includes: construction compliance, non-standard construction and construction of power grid facilities, untimely reconstruction of rural power grids, land occupation compensation equivalence and noise pollution.
Still further, the power supply quality appeal feature index sub-database includes: frequent power failure, low voltage, abnormal power quality, no power failure, and no power cut as planned.
Still further, the breakdown first-aid repair appeal feature index sub-database includes: the emergency repair service attitude is poor, the emergency repair quality is not high, and the emergency repair time is out of limit.
Still further, the business appeal characteristic index sub-database includes: meter reading error, outage notice, power restoration delay, meter line, business expansion installation overrun time limit and the like.
Still further, the service appeal feature index sub-database includes: the attitude of service personnel is not normal, the behavior is not normal, and the management of business halls and charging network points is not normal.
Further, the big data algorithm model comprises a linear regression model, and the quantitative relation of the interdependence between two or more variables is determined by utilizing regression analysis; the big data algorithm model comprises a Logistic regression model, a regression formula is established for a classification boundary according to the existing data, classification is carried out according to the regression formula, and optimal fitting is carried out through regression; the big data algorithm model comprises a decision tree model, a decision tree is constructed through training data, and unknown data are classified; the big data algorithm model comprises a clustering analysis model, non-hierarchical clustering is carried out based on distance, data are divided into preset class numbers K on the basis of a minimized error function, and the distance is used as an evaluation index of similarity; the big data algorithm model also includes a neural network model.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the invention can realize accurate characteristic identification of the power customer, and is convenient for enterprises to make a targeted service strategy to improve the customer service satisfaction. The invention can integrate various source data of a company, build a multi-dimensional and three-dimensional customer portrait by relying on a big data analysis technology, describe the deep level behavior characteristics of the customer through the label, provide personalized service for the customers with the same type of electric power, and provide data support for creating a novel customer management mode taking the customer label as the core.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by specific examples:
the invention relates to a user holographic portrait label generation method based on energy power big data, which utilizes big data processing technology to generate a user characteristic label, wherein the user characteristic label is generated by analyzing a characteristic appeal index of a user and utilizing a big data algorithm model; the basic database configuration of the big data processing technology is constructed around client appeal, opinions and consultation data streams of channels such as power 95598, power intranet extranet, power intranet, WeChat public line, business hall opinion book and the like are imported into the basic database to serve as original sources of label data, and the client is marked in a label mode through data analysis.
The database of the characteristic appeal index needs to be constructed first, and comprises the following sub-database branches: the power grid power supply system comprises a power grid construction appeal characteristic index sub-database, a power supply quality appeal characteristic index sub-database, a fault emergency repair appeal characteristic index sub-database, a business appeal characteristic index sub-database and a service appeal characteristic index sub-database; the power grid construction appeal characteristic index sub-database comprises: construction compliance, nonstandard construction and construction of power grid facilities, untimely rural power grid transformation, land occupation compensation equivalence and noise pollution; the power supply quality appeal characteristic index sub-database comprises: frequent power failure, low voltage, abnormal power quality, no power failure and power failure not according to a plan; the fault first-aid repair appeal characteristic index sub-database comprises: the emergency repair service attitude is poor, the emergency repair quality is not high, and the emergency repair time limit is exceeded; the business appeal characteristic index sub-database comprises: meter reading error, outage notice, power restoration delay, meter line, business expansion installation overrun time limit and other types; the service appeal feature index sub-database comprises: the attitude of service personnel is not normal, the behavior is not normal, and the management of business halls and charging network points is not normal.
The big data algorithm model can have a plurality of cross application options, including a linear regression model, and the quantitative relation of the interdependence between two or more variables is determined by utilizing regression analysis; the big data algorithm model comprises a Logistic regression model, a regression formula is established for a classification boundary according to the existing data, classification is carried out according to the regression formula, and optimal fitting is carried out through regression; the big data algorithm model comprises a decision tree model, a decision tree is constructed through training data, and unknown data are classified; the big data algorithm model comprises a clustering analysis model, non-hierarchical clustering is carried out based on distance, data are divided into preset class numbers K on the basis of a minimized error function, and the distance is used as an evaluation index of similarity.
The construction steps of the linear regression model comprise: collecting data: the system collects a large amount of historical data of the research object about the characteristic quantity; because regression analysis is a quantitative analysis method established on the basis of a large amount of data, the quantity and the accuracy of historical data directly influence the result of the regression analysis;
setting a regression equation: analyzing the relation between a large amount of historical data and setting a regression equation according to the rule expressed between independent variables and dependent variables; setting a regression equation is the key of a regression analysis method, and selecting an optimal model to set the regression equation is the basis for prediction by using the regression analysis method.
Determining a regression coefficient: substituting the known data into the set regression equation, calculating a regression coefficient by using a least square principle, and determining the regression equation;
and (4) carrying out correlation test: the correlation test refers to the test of the reliability of the determined regression equation capable of representing the correlation between independent variables and dependent variables, and comprises three methods, namely R test, t test and F test;
making a prediction and determining a confidence interval: after passing through the correlation test, predicting by using the determined regression equation; and giving a confidence interval of the single-point predicted value while carrying out single-point prediction.
The Logistic regression model establishes a regression formula for the classification boundary according to the existing data and classifies according to the regression formula, and the regression is the best fit; the Logistic regression model construction method comprises the following steps: A. setting a logistic regression architecture: 1) each regression coefficient is initialized to 1; 2) repeating for R times; 3) calculating the gradient of the whole data set; 4) updating the vector of the regression coefficient by using the step length x gradient; 5) returning a regression coefficient; B. setting a logistic regression algorithm flow: collecting data by an informatization method; preparing data, namely performing structured format conversion on the data because distance calculation is needed and the data type is required to be numerical; analyzing data, namely analyzing the data based on data use guidance; setting training aim to find out optimal classification regression coefficient; training to complete the feasibility and execution rate of the test algorithm; and (3) using an algorithm, namely firstly converting some data into corresponding structured numerical values, and then performing regression calculation on the numerical values based on the trained regression coefficients to finish classification and regression judgment of categories.
The Logistic regression model is used as a traditional statistical model, no requirement is provided for whether variables are subjected to normal distribution, independent variables can be continuous or discontinuous, the grading of credit risks due to the discontinuity of the dependent variables is most suitable, even if no linear relation exists between the credit risks and other related factors, people can also research make internal disorder or usurp, the calculation of future default probability of borrowers can be applied, the flexibility is high, and the economic significance is very practical. The Logistic regression model can be used for measuring and calculating various default rates under the condition of complete data.
Based on the number of dependent variables and the fact that the Logistic regression model does not require that independent variables obey normal distribution, an ordered Logistic regression model is selected, and if the dependent variables have M types, the regression model of M-1 dependent variables is fitted:
Figure BDA0003364559920000051
where Y represents the probability that an enterprise credit belongs to a certain level, XiThe i-th index representing the argument. Because the ordered Logistic regression model belongs to an accumulative function, the evaluation is carried out to obtain an accumulative Logist model:
Logit(Pi)＝ln[P(y≤j)/P(y≥j+1)]＝ai+bX (2)
where a represents the intercept of the model corresponding to the ith argument and b represents a set of regression coefficients corresponding to X.
From the results of the model output, an intercept term a and a coefficient term b are derived from the parameter estimates, and when Y is equal to a particular value, the probability of Y ═ j occurring can be found:
Figure BDA0003364559920000052
the interpretation for the coefficient b from the argument in the model is:
when b is 0, the independent variable X has no effect on Y;
when b > 0, P (y > j) is larger and P (y < j) is smaller, and when X increases, the probability of selecting a higher level increases and the probability of selecting a lower level decreases (assuming that j-l in the model is the lowest level).
When b < 0, P (y < j) is larger and P (y > j) is smaller, and when X is increased, the possibility of selecting a higher rank is reduced and the possibility of selecting a lower rank is increased.
Since Logistic regression belongs to the cumulative regression function, the cumulative probability ratio is reflected by the inverse logarithm of b:
if b > 0, exp (-b) < l, i.e., X increases by one unit, the cumulative probability decreases and the probability of a high selection level increases.
If b < 0, exp (-b) > l, i.e., X increases by one unit, the cumulative probability increases and the probability of a high selection level decreases.
Because the dependent variable relates to ordered multi-classification variables, ordered Logistic regression is selected, however, the model of the ordered Logistic regression is an accumulative regression function, the probability estimated by the model is the accumulative probability, and the accumulative probability is obtained to be compared with the effect of each independent variable to detect.
In the aspect of designing a Logistic regression model, the credit risk of a bond is analyzed by ordered Logistic regression, a certain preset is provided by combining actual influence factors, possible influence factors are found out, due to the fact that the factors are various, correlation possibly exists among all variables, in order to improve the accuracy of the model, the variables are firstly subjected to factor analysis, component factors are extracted by a dimension reduction method, therefore, the correlation among the variables is removed, and finally, the ordered Logistic regression is combined and carried out. The method comprises the steps of firstly, reducing the dimension of selected representative index data, adopting a maximum variance method, observing the accumulation probability and the characteristic value of an independent variable to obtain a principal component, analyzing a rotation matrix and a rotation load to classify the independent variable into the principal component, naming the principal component, and obtaining the relationship between a principal component factor and each independent variable from component coefficient scores. And secondly, after the index factors are subjected to factor analysis, obtaining main component factors, and naming and extracting the main component factors. And thirdly, integrating the main component factors obtained in the first two steps, and obtaining the data tag characteristics through ordered Logistic regression analysis.
The neural network model can cooperatively process large-scale distributed storage information and parallel information by simulating the structure and the function of a human brain neural network, and the ANN is essentially the simplification and abstract simulation of the human brain. The ANN has the capability of arbitrarily approximating a nonlinear system, can process the interconnection relation between nodes in the network through self-learning and self-adjustment, and continuously adjusts model parameters to adapt to the change of an external environment, thereby finally realizing the information learning and self-adapting functions. The neural network has the outstanding advantages of capability of processing the parallel distribution problem of a large-scale system, high dynamic response speed and strong learning and memory functions. These advantages are attributed to the topology of the ANN's own network and the processing power of the nodes. The ANN is used as a parallel system, and the operation speed of the network is high by means of a simple neuron structure and a node processing function. The neural network mainly has the following characteristics: (1) the associative memory capacity is strong, the fault tolerance is good, and the ANN neuron mechanism and the connection mode determine the associative memory characteristics of the ANN neuron mechanism. And the memory information is stored in the weight coefficient among the neurons in a node distributed mode. If the memory information is fuzzy or damaged, the neural network is not seriously influenced, so that the noise resistance and the fault tolerance of the system are high, and the training of the neural network samples can help process historical data with data defects within an error tolerance range. (2) The parallelism is strong, the neural network is composed of countless single neurons, and although the single neuron has a simple structure and a single function, the network can be formed to carry out a large amount of parallel operations, so that the information processing capability is enhanced. (3) The nonlinear is strong, the neural network is mainly characterized in that the neural network has arbitrary approximation capability to a nonlinear system, the external characteristics of the input and output ports of the neural network keep high nonlinearity, and the ANN can process complex logic operation and nonlinear problems, and the ANN generally applies a three-layer neural network to approximately express a nonlinear continuous function. (4) The self-learning performance is strong, the neural network can be learned and trained, and even if the external environment is changeable and complex, the neural network can also automatically adjust the network topology and the connection mode so as to adapt to the changeable external environment and enable the output effect to be closer to the reality.
The neuron mathematical expression is very important in a neuron and a network, and whether the neural network has the memory learning ability or not depends on the transfer function of the neural network except the relation with the nonlinear complex connection structure of the network. In order to distinguish from the automatic control theory and highlight the function role, the neural network transfer function is named as a start function, and the main role is as follows: (1) starting function control of input to output; (2) converting an input signal and an output signal; (3) for an infinite input, a clipping output effect can be achieved. The start-up function is typically a non-linear function. Common types of start-up functions are: threshold type, linear type, S type.
(1) The threshold type transfer function outputs an arbitrary input signal to an amplitude of 0 or 1 according to different properties, and the function is expressed as a unit step characteristic. At this time, the input-output expression of the artificial neuron is:
Figure BDA0003364559920000071
(2) in the piecewise linear type startup transfer function, the output of the network is equal to the weighted input plus the offset value, and the input-output expression of the function is:
A＝f(W*P+b)＝W*P+b
(3) the S-type starting function limits any input amplitude to a (0, 1) region, the function is monotonous and differentiable in the range, and the commonly used S-type function is an exponential function or a hyperbolic tangent function. The input-output expression of the function is:
Figure BDA0003364559920000081
according to different connection modes of neurons, neural networks can be divided into two types: a feedback-free forward network and an inter-combination type. The forward network includes input layer, intermediate layer, and output layer 3 sections, the intermediate layer may contain multiple layers, but the neurons of each layer map the output of the previous layer. For a combined network, the neurons are interconnected, so that information can be repeatedly learned and trained among the neurons, and finally, an input signal gradually tends to a certain stable state.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

Claims (8)

1. A user holographic portrait label generation method based on energy and power big data is characterized in that a user characteristic label is generated by utilizing a big data processing technology, the user characteristic label generates a database of characteristic appeal indexes by analyzing the characteristic appeal indexes of a user and utilizing a big data algorithm model; the basic database configuration of the big data processing technology is constructed around client appeal, opinions and consultation data streams of channels such as power 95598, power intranet extranet, power intranet, WeChat public line, business hall opinion book and the like are imported into the basic database to serve as original sources of label data, and the client is marked in a label mode through data analysis.
2. The method for generating the user hologram image label based on the energy power big data as claimed in claim 1, wherein: constructing a database of the characteristic appeal indexes, wherein the database comprises the following sub-database branches: the power grid construction appeal characteristic index sub-database, the power supply quality appeal characteristic index sub-database, the fault emergency repair appeal characteristic index sub-database, the business appeal characteristic index sub-database and the service appeal characteristic index sub-database.
3. The method for generating the user hologram image label based on the energy power big data as claimed in claim 2, wherein: the power grid construction appeal characteristic index sub-database comprises: construction compliance, non-standard construction and construction of power grid facilities, untimely rural power grid transformation, land occupation compensation equivalence and noise pollution.
4. The method for generating the user hologram image label based on the energy power big data as claimed in claim 2, wherein: the power supply quality appeal characteristic index sub-database comprises: frequent power failure, low voltage, abnormal power quality, no power failure, and no power cut as planned.
5. The method for generating the user hologram image label based on the energy power big data as claimed in claim 2, wherein: the fault first-aid repair appeal characteristic index sub-database comprises: the emergency repair service attitude is poor, the emergency repair quality is not high, and the emergency repair time limit is exceeded.
6. The method for generating the user hologram image label based on the energy power big data as claimed in claim 2, wherein: the business appeal characteristic index sub-database comprises: meter reading error, outage notice, power restoration delay, meter line, business expansion installation overrun time limit and the like.
7. The method for generating the user hologram image label based on the energy power big data as claimed in claim 2, wherein: the service appeal feature index sub-database includes: the attitude of service personnel is not normal, the behavior is not normal, and the management of business halls and charging network points is not normal.
8. The method for generating the user hologram image label based on the energy power big data as claimed in claim 1, wherein: the big data algorithm model comprises a linear regression model, and the quantitative relation of the interdependence between two or more variables is determined by utilizing regression analysis; the big data algorithm model comprises a Logistic regression model, a regression formula is established for a classification boundary according to the existing data, classification is carried out according to the regression formula, and optimal fitting is carried out through regression; the big data algorithm model comprises a decision tree model, a decision tree is constructed through training data, and unknown data are classified; the big data algorithm model comprises a clustering analysis model, non-hierarchical clustering is carried out based on distance, data are divided into preset class numbers K on the basis of a minimized error function, and the distance is used as an evaluation index of similarity; the big data algorithm model also includes a neural network model.
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