CN111428982A - High-quality photovoltaic customer evaluation and screening method based on big data - Google Patents
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Abstract
The invention provides a high-quality photovoltaic customer evaluation and screening method based on big data, which comprises the following steps: extracting user data related to customer power credit from detail data of the marketing system as index data; establishing an evaluation model containing index data; carrying out standardization processing on the index data; weighting the index data after the standardization processing based on an entropy weighting method; and calculating a score based on the weighted index data, and screening out high-quality customers according to a score result. An evaluation system containing multidimensional indexes is built through a big data means, evaluation deviation caused by screening of customers based on single indexes is avoided, high-quality customers are screened more scientifically and accurately, the high-quality customers are stimulated, and the healthy development of the photovoltaic industry is promoted.
Description
Technical Field
The invention belongs to the field of data analysis, and particularly relates to a high-quality photovoltaic customer evaluation and screening method based on big data.
Background
With the informatization development of the power industry, the quantity and types of power data are continuously increased, the data value is mined along with the arrival of the power big data era, high-quality customers are screened out through the power big data, the high-quality customers are stimulated, and the healthy development of the whole photovoltaic industry can be promoted. The traditional mining and screening system for the high-quality customers depends on single data types and limited quantity, screened results are prone to have deviation, and evaluation on the high-quality customers is not comprehensive.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a high-quality photovoltaic customer evaluation and screening method based on big data, which comprises the following steps:
extracting user data related to customer power credit from detail data of the marketing system as index data;
establishing an evaluation model containing index data;
carrying out standardization processing on the index data;
weighting the index data after the standardization processing based on an entropy weighting method;
and calculating a score based on the weighted index data, and screening out high-quality customers according to a score result.
Optionally, the extracting, from the detail data of the marketing system, user data related to the customer power credit as index data includes extracting the user data from three aspects of power quantity characteristics, payment behaviors and power consumption behaviors;
wherein, the change situation of the monthly electricity consumption of the client is analyzed through the electricity quantity characteristics;
analyzing the monthly electric charge payment condition of the client through the payment behavior;
and analyzing whether the customer is in compliance with the electricity utilization through the electricity utilization behavior.
Optionally, the normalizing the index data includes:
the index data was processed using the z-score normalization method, which was calculated as:
wherein z is the index data after the standardization, x is the index data before the standardization, mean (x) is the average value of the index data samples, std (x) is the standard deviation of the index data samples, and z, x, mean (x) and std (x) are positive integers.
Optionally, the weighting the normalized index data based on the entropy weighting method includes:
calculating an entropy value of the index data according to an entropy value calculation formula;
and weighting each item of index data according to an entropy weight calculation formula based on the obtained entropy value.
Further, the formula for calculating the entropy value is
Wherein e isiAs the entropy of the ith index dataThe value is large, n is the total number of observed values of the ith index data, VijThe j observation value of the ith index data; vijN is a positive integer, i and j are positive integers more than 1, and eiThe value range of (a) is between 0 and 1, and the closer the value is to 0, the greater the variation degree of the index data is, the more the information is covered.
Further, the entropy weight calculation formula is
Wherein, ω isiThe entropy weight of the ith index data is, and m is the total number of the index data;
ωiand m is a positive integer.
Optionally, the calculating a score based on the weighted index data, and screening out high-quality customers according to a score result includes:
calculating the grade of the client based on the weighted index data;
and screening out the customers with the scores higher than the judgment threshold k as high-quality customers according to a preset judgment threshold k.
The technical scheme provided by the invention has the beneficial effects that:
an evaluation system containing multidimensional indexes is built through a big data means, evaluation deviation caused by customer screening based on single indexes is avoided, a method capable of screening high-quality customers more comprehensively is provided, user data in the photovoltaic industry is fully mined and utilized, the screened high-quality customers are stimulated, and further healthy development of the photovoltaic industry is promoted.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a block diagram of a high-quality photovoltaic customer evaluation and screening method based on big data according to the present invention.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
As shown in fig. 1, the invention provides a high-quality photovoltaic customer evaluation and screening method based on big data, which includes:
s1, extracting user data related to the client power credit from the detail data of the marketing system as index data;
s2, establishing an evaluation model containing index data;
s3, standardizing the index data;
s4, weighting the index data after the standardization process based on the entropy weighting method;
and S5, calculating a score based on the weighted index data, and screening out high-quality customers according to the score result.
Through a big data analysis mode, more various user data are obtained, the user data in the photovoltaic industry are fully mined and utilized, a multi-dimensional evaluation analysis system for clients is established, evaluation deviation caused by client screening based on a single index is avoided, high-quality clients can be screened more comprehensively and scientifically to provide motivation development, and further the healthy development of the photovoltaic industry is promoted.
In the embodiment, the extracting of the user data related to the customer power credit from the detail data of the marketing system as the index data comprises extracting the user data from three aspects of power quantity characteristics, payment behaviors and power utilization behaviors;
the change situation of the monthly power consumption of the client is analyzed through the electric quantity characteristics, and the change situation is specifically divided into 3 indexes of power consumption fluctuation, capacity change and electricity price adjustment.
The monthly electric charge payment condition of the customer is analyzed through the payment behavior, and the monthly electric charge payment condition is specifically divided into 3 indexes of payment time, payment amount and payment mode.
Whether the customer complies with the electricity utilization is analyzed through the electricity utilization behavior, and the electricity utilization is specifically divided into default electricity utilization and 2 indexes of electricity stealing. The default electricity utilization can be refined into default electricity utilization types and default electricity utilization times, and the electricity stealing can be refined into electricity stealing types and electricity stealing times.
In this embodiment, the normalizing the index data includes:
the index data was processed using the z-score normalization method, which was calculated as:
wherein z is the index data after the standardization, x is the index data before the standardization, mean (x) is the average value of the index data samples, std (x) is the standard deviation of the index data samples, and z, x, mean (x) and std (x) are positive integers.
And processing the index data by a z-score standardization method to eliminate the data range and the difference between dimensions between different indexes so that the index data have the same average value and standard deviation.
In this embodiment, the weighting the normalized index data based on the entropy weighting method includes:
calculating an entropy value of the index data according to an entropy value calculation formula;
and weighting each item of index data according to an entropy weight calculation formula based on the obtained entropy value.
In this embodiment, the formula for calculating the entropy value is
Wherein e isiIs the size of entropy of the ith index data, n is the total number of observed values of the ith index data, VijThe j observation value of the ith index data;
Vijn is a positive integer, i and j are positive integers more than 1; said eiThe value range of (A) is between 0 and 1, and the closer to 0 the value is, the index number is representedThe larger the variation degree is, the more information is covered, and the more weight the corresponding data occupies.
The entropy weight calculation formula is
Wherein, ω isiThe entropy weight of the ith index data is, and m is the total number of the index data;
ωiis a positive number, and m is a positive integer.
The entropy weighting method carries out weighting according to the information content carried by the data, avoids the randomness problem brought by subjective weighting to a certain extent, and the weighting is more objective and more beneficial to improving the accuracy of the result.
In this embodiment, the calculating a score based on the weighted index data and screening out a high-quality client according to a score result includes:
calculating a customer score based on the weighted index data;
and screening out the customers with the scores higher than the judgment threshold k as high-quality customers according to a preset judgment threshold k.
And processing the customer scores according to an integer function to obtain final customer scores which are positive integers, and conveniently comparing the final customer scores with a preset threshold value.
For example, based on the calculated entropy weight, the index data corresponding to the customer to be evaluated is subjected to weighted calculation, and the final scoring result is obtained as follows:
customer 1 final score 80, customer 2 final score 69, customer 3 final score 93, customer 4 final score 84, customer 5 final score 76, customer 6 final score 65, customer 7 final score 87, customer 8 final score 103, customer 9 final score 42, customer 10 final score 59. And if the judgment threshold k is preset to be 85, the high-quality clients are selected to be the client 3, the client 7 and the client 8 through comparison.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A high-quality photovoltaic customer evaluation and screening method based on big data is characterized by comprising the following steps:
extracting user data related to customer power credit from detail data of the marketing system as index data;
establishing an evaluation model containing index data;
carrying out standardization processing on the index data;
weighting the index data after the standardization processing based on an entropy weighting method;
and calculating a score based on the weighted index data, and screening out high-quality customers according to a score result.
2. The method for evaluating and screening high-quality photovoltaic customers based on big data as claimed in claim 1, wherein the extracting of the user data related to the customer power credit from the detail data of the marketing system as index data comprises extracting the user data from three aspects of power quantity characteristics, payment behaviors and power utilization behaviors;
wherein, the change situation of the monthly electricity consumption of the client is analyzed through the electricity quantity characteristics;
analyzing the monthly electric charge payment condition of the client through the payment behavior;
and analyzing whether the customer is in compliance with the electricity utilization through the electricity utilization behavior.
3. The big data-based high-quality photovoltaic customer evaluation and screening method according to claim 1, wherein the standardization of the index data comprises:
the index data was processed using the z-score normalization method, which was calculated as:
wherein z is the index data after the standardization, x is the index data before the standardization, mean (x) is the average value of the index data samples, std (x) is the standard deviation of the index data samples, and z, x, mean (x) and std (x) are positive integers.
4. The big-data-based high-quality photovoltaic client evaluation and screening method according to claim 1, wherein the weighting of the normalized index data based on entropy weighting comprises:
calculating an entropy value of the index data according to an entropy value calculation formula;
and weighting each item of index data according to an entropy weight calculation formula based on the obtained entropy value.
5. The big-data-based high-quality photovoltaic customer evaluation and screening method according to claim 4, wherein the entropy calculation formula is
Wherein e isiIs the size of entropy of the ith index data, n is the total number of observed values of the ith index data, VijThe j observation value of the ith index data;
Vijn is a positive integer, i and j are positive integers more than 1, and eiThe value range of (a) is between 0 and 1, and the closer the value is to 0, the greater the variation degree of the index data is, the more the information is covered.
6. The big-data-based high-quality photovoltaic customer evaluation and screening method according to claim 5, wherein the entropy weight calculation formula is
Wherein, ω isiThe entropy weight of the ith index data is, and m is the total number of the index data; omegaiIs a positive number, and m is a positive integer.
7. The method for evaluating and screening high-quality photovoltaic clients based on big data as claimed in claim 1, wherein the step of calculating a score based on the weighted index data and screening high-quality clients according to the score result comprises:
calculating the grade of the client based on the weighted index data;
and screening out the customers with the scores higher than the judgment threshold k as high-quality customers according to a preset judgment threshold k.
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Cited By (2)
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| CN113822596A (en) * | 2021-10-12 | 2021-12-21 | 深圳市我们在线教育有限公司 | Customer screening method based on big data |
| CN117474444A (en) * | 2023-09-27 | 2024-01-30 | 广州交通集团物流有限公司 | Digital medicine supply chain management platform |
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