CN113052483A - Credit analysis method based on electric power big data - Google Patents

Abstract

A credit analysis method based on electric power big data. The credit analysis method based on the electric power big data is used for guaranteeing the reliability of credit analysis by analyzing and calculating the electric power consumption data of the enterprise. The method comprises the following steps: s1, preprocessing the electricity consumption data, S2, dividing the electricity consumption data into evaluation indexes and calculating layer by layer; and S3, summarizing and calculating credit investigation scores. The invention quantitatively analyzes the enterprise operation condition based on the electricity consumption big data, and further helps credit institutions and credit investigation institutions such as banks and financial institutions to accurately evaluate the performance capability and performance willingness of the enterprise, thereby forming quantitative and reliable credit evaluation. Considering the risk of losing credit within the capacity range of the credit object, the integrity behavior analysis such as electricity consumption behavior specification and payment behavior is also listed in the credit evaluation. The proposal of the credit assessment is of far-reaching significance for further stable development of economy and establishment of honest society.

Description

Credit analysis method based on electric power big data

Technical Field

The invention relates to the field of big data, in particular to a credit analysis method based on electric power big data.

Background

Power is the practice of big data concepts, techniques and methods in the power industry. The large electric power data relates to links of power generation, power transmission, power transformation, power distribution, power utilization and scheduling, and is cross-unit, cross-professional and cross-service data analysis and mining and data visualization.

The application of the large electric power data is to fuse with information such as macroscopic economy, people's life, social security, road traffic and the like to promote the development of the economic society on one hand, and is to fuse data of cross-profession, cross-unit and cross-department in the electric power industry or enterprise on the other hand to promote the management level and the economic benefit of the industry and the enterprise.

With the continuous promotion of the establishment of the social credit system, the establishment of the credit evaluation system of enterprises and individuals in China becomes the key development direction in China. Therefore, it is necessary to apply the power data to credit investigation to satisfy effective evaluation of the power consumption of the enterprise.

Disclosure of Invention

Aiming at the problems, the invention provides a credit analysis method based on electric power big data, which ensures the reliability of credit analysis by analyzing and calculating the electric power consumption data of enterprises.

The technical scheme of the invention is as follows: the method comprises the following steps:

s1, preprocessing the electricity consumption data;

s2, dividing the electricity consumption data into evaluation indexes and calculating layer by layer;

and S3, summarizing and calculating credit investigation scores.

In step S1, denoising the power consumption data of the user by using a rloess method.

In the step S2, in the step S,

the evaluation indexes comprise four primary indexes of electricity consumption, electricity consumption behavior, interaction behavior, payment behavior and electricity consumption capacity;

wherein, the electricity consumption comprises three secondary indexes of business situation, production arrangement and development situation,

the power consumption score = the sum of the product of the business situation score, the production arrangement score and the development situation score and the corresponding weight respectively;

electricity usage score = default electricity stealing behavior score;

the interactive behavior comprises two secondary indexes of appeal frequency and complaint frequency,

the interactive behavior score = the sum of the product of the complaint frequency score and the corresponding weight respectively;

the payment behavior comprises three secondary indexes of a default fee generation record, a fee control limit and an overdue unpaid electric fee record,

the payment behavior score = sum of the record score of the default deposit generation, the charge control limit score and the overdue unpaid electric charge record score multiplied by corresponding weight respectively;

the power utilization capacity comprises two secondary indexes of capacity expansion record and capacity reduction record,

capacity score = sum of volume record score and volume reduction record score, respectively, multiplied by the corresponding weights.

In the step S3, in the step S,

the credit investigation score is: and the electricity consumption score, the electricity consumption behavior score, the interaction behavior score, the payment behavior score and the electricity consumption capacity score are respectively summed with the products of the corresponding weights.

In step S2, the secondary index score of the electricity consumption is:

operating situations:

the calculation method comprises the following steps: the sum of the interval months/number of intervals between peak months = a,

usage data and range: the power consumption of the enterprise is about three months, and the power consumption of the enterprise is about three months;

and (4) judging the standard:

a > 6, conclusion: the peak density is sparse, and the operation is stable; scoring: 100 minutes;

a = [4, 6], conclusion: peak density is general, and business conditions are general; scoring 80 points;

a = (— ∞, 4), conclusion: the peak density is dense, and the operation condition is to be improved; the score is 60 points;

production arrangement:

the calculation method comprises the following steps: valley total charge/peak total charge = b,

usage data and range: the power consumption of the enterprise is about three months, and the power consumption of the enterprise is about three months;

and (4) judging the standard:

b = (∞, 0.2), conclusion: the production arrangement is reasonable; scoring: 100 minutes;

b = [0.2, 0.5], conclusion: the production condition is general; scoring: 80 minutes;

b = (0.5, + ∞), conclusion: the production arrangement needs to be improved urgently; scoring: 60 minutes;

③ development situation:

the calculation method comprises the following steps: electricity consumption data for the last six months compares = c,

usage data and range: the electricity consumption of the enterprise is about three months;

and (4) judging the standard:

c = (0.2, + ∞), conclusion: the development is in a rapid rising trend; scoring: 100 minutes;

c = [0, 0.2], conclusion: the development is in an ascending trend; scoring: 80 minutes;

c = [ -0.8, 0), conclusion: the development of the road is in the trend of a downhill road; scoring: 60 minutes;

c = (— ∞, -0.8), conclusion: developing a severe landslide trend; scoring: and 40 minutes.

In step S2, the electricity consumption behavior is scored as:

the calculation method comprises the following steps: the number of defaulting steals = d,

usage data and range: the number of times of default electricity stealing of enterprises in the last three years,

and (4) judging the standard:

d = [0, 1), score: 100 minutes;

d = [1, + ∞ ], score: and 0 point.

In step S2, the secondary index score of the interaction behavior is:

appeal times are as follows:

the calculation method comprises the following steps: appeal times;

usage data and range: the number of complaints of the enterprise in the last three years = e;

and (4) judging the standard:

e = (4, + ∞), score: 100 minutes;

e = [3, 4], score: 80 minutes;

e = (0, 3), score: 60 minutes;

e =0 score: 0 minute;

the complaint times are as follows:

the calculation method comprises the following steps: appeal times;

usage data and range: the number of complaints of the enterprise in the last three years = f;

and (4) judging the standard:

f = [0, 2], score: 100 minutes;

f = (2, 3], score: 80 points;

f = (3, 6], score: 60 points;

f = (6, + ∞), score: and 0 point.

In step S2, the secondary index score of the payment behavior is:

generating records of default gold:

the calculation method comprises the following steps: number of default gold times;

usage data and range: the number of times of default fund generation of the enterprise in the last three years = g;

and (4) judging the standard:

g =0, score: 100 minutes;

g =1, score: 80 minutes;

g = [2, 3], score: 60 minutes;

g = (3, + ∞), score: 0 minute;

charging and controlling the amount:

the calculation method comprises the following steps: a charge control limit;

usage data and range: the enterprise charge control limit value = h;

and (4) judging the standard:

h = (10000, + ∞), score: 100 minutes;

h = [5000, 10000], score: 80 minutes;

h = (0, 5000), score: 60 minutes;

h =0, score: 0 minute;

third, recording overdue unpaid electric charge:

the calculation method comprises the following steps: the number of times of overdue unpaid electricity charges;

usage data and range: the number of overdue unpaid electricity charges of the enterprise = i;

and (4) judging the standard:

i =, 0 score: 100 minutes;

i =1, score: 80 minutes;

i = [2, 3], score: 60 minutes;

i = (3, + ∞), score: and 0 point.

In step S2, the secondary index score of the power consumption capacity is:

carrying out capacity expansion recording:

the calculation method comprises the following steps: the number of expansion times;

usage data and range: the number of enterprise expansion times = j;

and (4) judging the standard:

j >0, score: 100 minutes;

j =0, score: 0 minute;

volume reduction recording:

the calculation method comprises the following steps: the number of volume reduction times;

usage data and range: enterprise volume reduction times = k;

and (4) judging the standard:

k =0, score: 100 minutes;

k >0, score: and 0 point.

The invention quantitatively analyzes the enterprise operation condition based on the electricity consumption big data, and further helps credit institutions and credit investigation institutions such as banks and financial institutions to accurately evaluate the performance capability and performance willingness of the enterprise, thereby forming quantitative and reliable credit evaluation. Considering the risk of losing credit within the capacity range of the credit object, the integrity behavior analysis such as electricity consumption behavior specification and payment behavior is also listed in the credit evaluation. The proposal of the credit assessment is of far-reaching significance for further stable development of economy and establishment of honest society.

Detailed Description

The invention comprises the following steps in operation:

s1, preprocessing the electricity utilization data;

the data processed by the invention is mainly the electric quantity data of each user. In the data processing process, on one hand, the influence of the production period on the electricity consumption of the user is considered, on the other hand, the abnormal data of the electricity consumption caused by the power supply condition, the metering error and the artificial error are considered, the electricity consumption curve of each user needs to be smoothed, and the electricity consumption condition of the user is convenient to summarize.

By contrast, the rloess method can obtain better effect when denoising the power consumption data of the user, so the invention selects the rloess method to denoise, selects a larger window to calculate to obtain a smoother curve, and only needs to reduce the window size if only needing to eliminate abnormal data interference.

S2, data usage:

firstly, refining index calculation:

1, electricity consumption:

operating situations:

the calculation method comprises the following steps: the sum of the interval months/number of intervals between peak months = a,

usage data and range: 1, the electricity consumption of the enterprise is 2 in nearly three months, and the electricity utilization ring ratio data of the enterprise is nearly three months;

and (4) judging the standard:

a > 6 conclusion: the peak density is sparse, and the operation is stable; scoring: 100 minutes;

a = [4, 6] conclusion: peak density is general, and business conditions are general; scoring 80 points;

a = (— ∞, 4) conclusion: the peak density is dense, and the operation condition is to be improved; the score is 60 points;

production arrangement:

the calculation method comprises the following steps: valley total charge/peak total charge = b;

usage data and range: 1, the electricity consumption of the enterprise is 2 in nearly three months, and the electricity utilization ring ratio data of the enterprise is nearly three months;

and (4) judging the standard:

b = (— ∞, 0.2) conclusion: the production arrangement is reasonable; scoring: 100 minutes;

b = [0.2, 0.5] conclusion: the production condition is general; scoring: 80 minutes;

b = (0.5, + ∞) conclusion: the production arrangement needs to be improved urgently; scoring: 60 minutes;

③ development situation:

the calculation method comprises the following steps: electricity consumption data for the last six months year = c;

usage data and range: 1, the power consumption of the enterprise is nearly three months;

and (4) judging the standard:

c = (0.2, + ∞) conclusion: the development is in a rapid rising trend; scoring: 100 minutes;

c = [0, 0.2] conclusion: the development is in an ascending trend; scoring: 80 minutes;

c = [ -0.8, 0) conclusion: the development of the road is in the trend of a downhill road; scoring: 60 minutes;

c = (— ∞, -0.8) conclusion: developing a severe landslide trend; scoring: 40 minutes;

electricity consumption score (a) = business situation score 0.4+ production schedule score 0.2+ development situation score 0.4;

2, power consumption behavior

The calculation method comprises the following steps: number of defaulting electricity stealing = d;

usage data and range: the number of times of default electricity stealing of enterprises in three years;

and (4) judging the standard:

d = [0, 1) score: 100 minutes;

d = [1, + ∞ ] score: 0 minute;

electricity usage score (B) = default electricity stealing behavior score;

3, interactive behavior

First, the number of appeals

The calculation method comprises the following steps: appeal times;

usage data and range: the number of complaints of the enterprise in the last three years = e;

and (4) judging the standard:

e = (4, + ∞) score: 100 minutes;

e = [3, 4] score: 80 minutes;

e = (0, 3) score: 60 minutes;

e =0 score: 0 minute;

number of complaints

The calculation method comprises the following steps: number of appeals

Usage data and range: the number of complaints of the enterprise in the last three years = f;

and (4) judging the standard:

f = [0, 2] score: 100 minutes;

f = (2, 3] score: 80 points;

f = (3, 6] score: 60 points;

f = (6, + ∞) score: 0 minute;

interaction behavior score (C) = complaint times score 0.5+ complaint times score 0.5;

4, payment behavior

Record of the generation of default gold

The calculation method comprises the following steps: number of defaults

Usage data and range: the number of times of default fund generation of the enterprise in the last three years = g;

and (4) judging the standard:

g =0 score: 100 minutes;

g =1 score: 80 minutes;

g = [2, 3] score: 60 minutes;

g = (3, + ∞) score: 0 minute;

② charge control limit

The calculation method comprises the following steps: fee control limit

Usage data and range: the enterprise charge control limit value = h;

and (4) judging the standard:

h = (10000, + ∞) score: 100 minutes;

h = [5000, 10000] score: 80 minutes;

h = (0, 5000) score: 60 minutes;

h =0 score: 0 minute;

third, record of overdue unpaid electric charge

The calculation method comprises the following steps: number of overdue unpaid electric charges

Usage data and range: the number of overdue unpaid electricity charges of the enterprise = i;

and (4) judging the standard:

i =0 score: 100 minutes;

i =1 score: 80 minutes;

i = [2, 3] score: 60 minutes;

i = (3, + ∞) score: 0 minute;

the payment behavior score (D) = default gold generation record score 0.4+ fee control limit score 0.2+ overdue unpaid electricity fee record score 0.4;

5, capacity of using electricity

Expanding record

The calculation method comprises the following steps: number of capacity expansion

Usage data and range: the number of enterprise expansion times = j;

and (4) judging the standard:

j >0 score: 100 minutes;

j =0 score: 0 minute;

② volume reduction recording

The calculation method comprises the following steps: number of volume reductions

Usage data and range: enterprise volume reduction times = k;

and (4) judging the standard:

k =0 score: 100 minutes;

k >0 score: 0 minute;

capacity score (E) = augmented records score 0.5+ reduced records score 0.5;

s3, final credit score calculation, as in table 1:

credit score = a × 0.5+ B × 0.1 + C × 0.1 + D × 0.2+ E × 0.1.

According to the invention, by analyzing and calculating the power consumption data of the enterprise, the power consumption condition of the enterprise can accurately reflect the operation and production conditions of the enterprise, the feasibility of the analysis method is ensured, and meanwhile, the power consumption data of the enterprise is in a continuous updating state, so that the practicability of the analysis method is ensured.

In specific applications, the weights may be dynamically modified:

and analyzing the scores by combining the branch indexes, and dynamically changing the weight.

When the power consumption score is lower than 50 minutes, the weight of the index of the power consumption in the total score is increased if the enterprise development situation is judged not to be ideal.

When the payment behavior score is lower than 50 minutes, the weighting of the total score is emphasized if some problems exist in the cash flow of the enterprise.

The above embodiments are only embodiments disclosed in the present disclosure, but the scope of the disclosure is not limited thereto, and the scope of the disclosure should be determined by the scope of the claims.

Claims (9)

1. The credit analysis method based on the electric power big data is characterized by comprising the following steps of:

s1, preprocessing the electricity consumption data;

s2, dividing the electricity consumption data into evaluation indexes and calculating layer by layer;

and S3, summarizing and calculating credit investigation scores.

2. The credit analysis method based on power big data as claimed in claim 1, wherein in step S1, the power consumption data of the user is denoised by rloess method.

3. The electric power big data-based credit analysis method according to claim 1, wherein, in step S2,

the evaluation indexes comprise four primary indexes of electricity consumption, electricity consumption behavior, interaction behavior, payment behavior and electricity consumption capacity;

wherein, the electricity consumption comprises three secondary indexes of business situation, production arrangement and development situation,

the power consumption score = the sum of the product of the business situation score, the production arrangement score and the development situation score and the corresponding weight respectively;

electricity usage score = default electricity stealing behavior score;

the interactive behavior comprises two secondary indexes of appeal frequency and complaint frequency,

the interactive behavior score = the sum of the product of the complaint frequency score and the corresponding weight respectively;

the payment behavior comprises three secondary indexes of a default fee generation record, a fee control limit and an overdue unpaid electric fee record,

the payment behavior score = sum of the record score of the default deposit generation, the charge control limit score and the overdue unpaid electric charge record score multiplied by corresponding weight respectively;

the power utilization capacity comprises two secondary indexes of capacity expansion record and capacity reduction record,

capacity score = sum of volume record score and volume reduction record score, respectively, multiplied by the corresponding weights.

4. The electric power big data-based credit analysis method according to claim 3, wherein, in step S3,

the credit investigation score is: and the electricity consumption score, the electricity consumption behavior score, the interaction behavior score, the payment behavior score and the electricity consumption capacity score are respectively summed with the products of the corresponding weights.

5. The electric power big data-based credit analysis method according to claim 3, wherein in step S2, the secondary index score of the used electric quantity is:

operating situations:

the calculation method comprises the following steps: the sum of the interval months/number of intervals between peak months = a,

usage data and range: the power consumption of the enterprise is about three months, and the power consumption of the enterprise is about three months;

and (4) judging the standard:

a > 6, conclusion: the peak density is sparse, and the operation is stable; scoring: 100 minutes;

a = [4, 6], conclusion: peak density is general, and business conditions are general; scoring 80 points;

a = (— ∞, 4), conclusion: the peak density is dense, and the operation condition is to be improved; the score is 60 points;

production arrangement:

the calculation method comprises the following steps: valley total charge/peak total charge = b,

usage data and range: the power consumption of the enterprise is about three months, and the power consumption of the enterprise is about three months;

and (4) judging the standard:

b = (∞, 0.2), conclusion: the production arrangement is reasonable; scoring: 100 minutes;

b = [0.2, 0.5], conclusion: the production condition is general; scoring: 80 minutes;

b = (0.5, + ∞), conclusion: the production arrangement needs to be improved urgently; scoring: 60 minutes;

③ development situation:

the calculation method comprises the following steps: electricity consumption data for the last six months compares = c,

usage data and range: the electricity consumption of the enterprise is about three months;

and (4) judging the standard:

c = (0.2, + ∞), conclusion: the development is in a rapid rising trend; scoring: 100 minutes;

c = [0, 0.2], conclusion: the development is in an ascending trend; scoring: 80 minutes;

c = [ -0.8, 0), conclusion: the development of the road is in the trend of a downhill road; scoring: 60 minutes;

c = (— ∞, -0.8), conclusion: developing a severe landslide trend; scoring: and 40 minutes.

6. The electric power big data-based credit analysis method according to claim 3, wherein in step S2, the rating of the electricity consumption behavior is as follows:

the calculation method comprises the following steps: the number of defaulting steals = d,

usage data and range: the number of times of default electricity stealing of enterprises in the last three years,

and (4) judging the standard:

d = [0, 1), score: 100 minutes;

d = [1, + ∞ ], score: and 0 point.

7. The electric power big data-based credit analysis method according to claim 3, wherein in step S2, the secondary index score of the interactive behavior is:

appeal times are as follows:

the calculation method comprises the following steps: appeal times;

usage data and range: the number of complaints of the enterprise in the last three years = e;

and (4) judging the standard:

e = (4, + ∞), score: 100 minutes;

e = [3, 4], score: 80 minutes;

e = (0, 3), score: 60 minutes;

e =0 score: 0 minute;

the complaint times are as follows:

the calculation method comprises the following steps: appeal times;

usage data and range: the number of complaints of the enterprise in the last three years = f;

and (4) judging the standard:

f = [0, 2], score: 100 minutes;

f = (2, 3], score: 80 points;

f = (3, 6], score: 60 points;

f = (6, + ∞), score: and 0 point.

8. The electric power big data-based credit analysis method according to claim 3, wherein in step S2, the secondary index score of the payment behavior is:

generating records of default gold:

the calculation method comprises the following steps: number of default gold times;

usage data and range: the number of times of default fund generation of the enterprise in the last three years = g;

and (4) judging the standard:

g =0, score: 100 minutes;

g =1, score: 80 minutes;

g = [2, 3], score: 60 minutes;

g = (3, + ∞), score: 0 minute;

charging and controlling the amount:

the calculation method comprises the following steps: a charge control limit;

usage data and range: the enterprise charge control limit value = h;

and (4) judging the standard:

h = (10000, + ∞), score: 100 minutes;

h = [5000, 10000], score: 80 minutes;

h = (0, 5000), score: 60 minutes;

h =0, score: 0 minute;

third, recording overdue unpaid electric charge:

the calculation method comprises the following steps: the number of times of overdue unpaid electricity charges;

usage data and range: the number of overdue unpaid electricity charges of the enterprise = i;

and (4) judging the standard:

i =, 0 score: 100 minutes;

i =1, score: 80 minutes;

i = [2, 3], score: 60 minutes;

i = (3, + ∞), score: and 0 point.

9. The electric power big data-based credit analysis method according to claim 3, wherein in step S2, the secondary index score of the power consumption capacity is:

carrying out capacity expansion recording:

the calculation method comprises the following steps: the number of expansion times;

usage data and range: the number of enterprise expansion times = j;

and (4) judging the standard:

j >0, score: 100 minutes;

j =0, score: 0 minute;

volume reduction recording:

the calculation method comprises the following steps: the number of volume reduction times;

usage data and range: enterprise volume reduction times = k;

and (4) judging the standard:

k =0, score: 100 minutes;

k >0, score: and 0 point.