CN111027845A - Label model suitable for power market main part customer portrait - Google Patents

Label model suitable for power market main part customer portrait Download PDF

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CN111027845A
CN111027845A CN201911238257.XA CN201911238257A CN111027845A CN 111027845 A CN111027845 A CN 111027845A CN 201911238257 A CN201911238257 A CN 201911238257A CN 111027845 A CN111027845 A CN 111027845A
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杨怡静
严明辉
周崇东
王雪晋
陈井锐
王吉飞
郑瑛
胡辰
季天瑶
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Abstract

The invention relates to a label model suitable for a power market main body customer portrait, which is based on relevant data and information of a power market main body, utilizes the relation between the relevant data and the market main body to construct a label model suitable for the market main body customer portrait, sets indexes based on the collected relevant data and information, calculates weight and index values according to the indexes, and further sets classification values of the indexes to obtain labels reflecting the characteristics of the market main body.

Description

Label model suitable for power market main part customer portrait
Technical Field
The invention relates to the field of electric power, in particular to a label model suitable for a customer portrait of an electric power market main body.
Background
The existing electric power market generally evaluates the market main bodies by a credit rating mode, and the evaluation mode is generally subjective and difficult to deal with a large number of market main bodies. Aiming at the problems that the evaluation mode is subjective and the market main bodies with huge number are difficult to deal with in the prior art, the invention constructs a label model suitable for the customer portrait of the power market main body by utilizing the relevant data and information of the power market main body and utilizing the relation between the relevant data and the market main body.
Disclosure of Invention
In view of the technical problems in the prior art, a primary object of the present invention is to provide a tag model suitable for a customer figure of a power market subject. Based on the purpose, the invention at least provides the following technical scheme:
a label model adapted for a customer portrait of a power market subject, the power market subject including a power selling company, a customer, and a power plant, comprising:
the system comprises an information input module, a data processing module and a data processing module, wherein the information input module is used for inputting basic information of an electric power market main body, and the basic information comprises at least one of basic characteristics, operation level, financial risk, comprehensive information and transaction capacity of the electric power market main body; the processing module is connected with the information input module and used for receiving the basic information and selecting a standard sample, setting a primary index, a secondary index or a special index for the basic information, setting a weight for the primary index, the secondary index or the special index according to the standard sample, and calculating an index value according to a preset model; calculating a label value according to a preset model, setting a classification value of the index according to the preset model, and determining a label reflecting the main characteristics of the power market according to the classification value; and the output module is connected with the processing module and used for receiving the label and the label value and outputting the label and the label value corresponding to the electric power market main body.
Further, the primary index is directly extracted from the basic information, the secondary index is obtained by calculating the primary index data, the special index is a specific value, and the primary index, the secondary index and the special index all include an index name, index data and an index value.
Further, the calculation of the index value is a product of the index weight and the index data.
Further, the calculation of the tag value is the sum of the index values corresponding to the tag.
Further, the setting of the weight includes:
and taking the index weight and the label classification distinguishing value which are obtained by calculating the standard sample in the first evaluation period as the index weight and the label classification distinguishing value of the five future evaluation periods including the evaluation period, and updating the standard sample by using a random number generation function every five evaluation periods.
Further, the weight of the index is obtained by entropy weight calculation:
dividing the indexes into an ultra-large index, an ultra-small index, an intermediate index and an interval index, carrying out forward processing on all the indexes, carrying out standardization processing on the indexes subjected to forward processing, then carrying out normalization processing on the indexes subjected to standardization processing, calculating the entropy values of the indexes according to the indexes subjected to normalization processing, and calculating the weights of all the indexes according to the entropy values.
Further, the set of the classification value is: and performing descending order arrangement on the label values of the market main bodies in the standard sample, respectively finding out the label value of an integer part with the serial number being 0.1 times and the label value of an integer part with the serial number being 0.9 times of the number of the market main bodies in the standard sample in the descending order arrangement, taking the label value of the integer part with the serial number being 0.1 times as a first distinguishing value, and taking the label value of the integer part with the serial number being 0.9 times as a second distinguishing value.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention is suitable for a label model of a power market main body customer portrait, the label model suitable for the market main body customer portrait is constructed by utilizing the relation between relevant data and a market main body based on the relevant data and information of the power market main body, the model sets indexes based on the collected relevant data and information, calculates the weight and the index value according to the indexes, and further sets the classification value of the indexes to obtain the label reflecting the characteristics of the market main body.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, which are only some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by persons of ordinary skill in the art without any creative effort belong to the protection scope of the present invention.
The invention provides a label model suitable for customer figures of a power market main body, which is suitable for power market main bodies of power selling companies, users and power plants. The model comprises an information input module, a processing module and an output module.
The information input module is used for inputting basic information of the electric power market main body, and the basic information comprises at least one of basic characteristics, operation level, financial risk, comprehensive information and transaction capability information of the electric power market main body. The basic characteristics of the electric power market main body can be obtained from archive information, business registration information, a power grid company marketing integrated system, an electric power market trading system and a government credit system which are provided in an admission electric power market stage; the financial risk information can be obtained from financial statements of market subjects and bank running water; the trading capacity information is obtained from profile information provided by the market subject's admitted electricity market stage, the electricity market trading system. The comprehensive information is obtained from the archive information and the electric power market trading system. The operation level information is obtained from the certification information and the archive information submitted by the market main body.
The processing module is connected with the information input module and used for receiving input basic information of the electric power market main body, selecting a standard sample and setting a primary index, a secondary index or a special index for the basic information. The primary index, the secondary index and the special index all comprise index names, index data and index values. The primary index is information directly extracted from the basic information. The secondary index is obtained by calculating the primary index data. The value method of the special index is shown in table 3, and it can be known from table 3 that the special index can be a certain known specific value, 0 or 1. The indexes are different according to different market main bodies, some indexes are suitable for three types of main bodies, some indexes are only suitable for two types of main bodies, and some indexes are only suitable for one type of main bodies. And constructing the relation between the dimension, the label and the index according to the dimension to be inspected of the market main body, namely the basic information of the power market main body. The corresponding relation among the basic information, the labels and the indexes of the electric power market main body is shown in table 1, wherein 'three types' indicate that the indexes are applicable to three types of main bodies, namely an electric power selling company, a user and an electric power plant. The secondary indexes and secondary index formulas related to the model are given in table 2. Table 3 gives the numerical value access for the particular index. If the data in tables 1, 2 and 3 are not specifically described, the process data is the data in the evaluation period, and the section data is the data in the evaluation time window of the round. In one embodiment, the target subject customer net increase ratio is customer churn amount/total customer amount, wherein the customer churn amount is the total churn amount of the target subject customer within the evaluation period, and the total customer amount is the total target subject customer amount available in the evaluation time window.
TABLE 1
Figure RE-GDA0002368121170000041
Figure RE-GDA0002368121170000051
Figure RE-GDA0002368121170000061
Figure RE-GDA0002368121170000071
The secondary index and its corresponding calculation formula are shown in table 2.
TABLE 2
Figure RE-GDA0002368121170000081
Figure RE-GDA0002368121170000091
The specific indexes and the applicable index value taking method thereof are shown in table 3.
TABLE 3
Index name Index value taking method
Document normative score Establishing an archive normative credit evaluation system to obtain scores
Electric power engineering design and construction capability Evaluating the certification document provided by the evaluation target subject to obtain a numerical value
Power management capability Evaluating the certification document provided by the evaluation target subject to obtain a numerical value
Energy saving management capability Evaluating the certification document provided by the evaluation target subject to obtain a numerical value
Demand side management capability Evaluating the certification document provided by the evaluation target subject to obtain a numerical value
Enterprise change Enterprise Change set 1, unchanged set 0
Settlement account change Settlement 1 is set when the settlement place is changed, and 0 is set when the settlement place is not changed
Setting weights for the primary index, the secondary index or the special index according to a standard sample, and calculating an index value according to a preset model; calculating a label value according to a preset model, setting a classification value of the index according to the preset model, and determining a label reflecting the main characteristics of the power market according to the classification value
Wherein, the calculation of the index value is the product of the weight of the index and the index data. And setting corresponding weight for each index, wherein the weight is the contribution of the index to the label and can be a positive number or a negative number. Similarly, the discrimination value may be set to a negative number. The calculation of the label value is the sum of the index values corresponding to the label. An index of an unspecified type can directly determine the label.
Let the number of some market main bodies be a, the number is marked as 0-a, and [0.2 a ] is an integer part of 0.2 a ] and is generated by a random number generating function. In one embodiment, the random numbers are generated using a Matlab random number generation function.
And selecting the market main body with the number corresponding to the random number as a standard sample for setting the index weight and the label classification value. And the index weight and the label distinguishing value calculated by the standard sample in the first evaluation period are used as the index weight and the label distinguishing value of the five future evaluation periods including the evaluation period, and the standard sample is updated by applying a random number generation function every five evaluation periods.
The weight of the above index can be obtained by entropy weight method:
let n be [0.2 a ]]N is the number of the main bodies in the selected standard sample, and if a certain label Y corresponds to m indexes, x isijIndex data of the jth index corresponding to the ith main body in the standard sample under the label Y, i is less than or equal to n, i belongs to z*；≤m,j∈z*。
The indicators of the present invention can be classified into four categories, namely, the indicator type in table 1: the index is very large, namely the index with larger numerical value is better; the extremely small index is the index with smaller numerical value and better numerical value; the intermediate indicator, i.e., the closer to a certain value, the better, in one embodiment, the closer to 1, the better; the interval type index falls best in a certain interval. "good" here means that the contribution to the label is large. In one embodiment, the power plant interval is set to [ 40%, 50% ], the electricity vendor interval is set to [ 50%, 60% ], and the customer interval is set to [ 40%, 60% ]. The four types of indexes are converted into forward indexes, and all the indexes are converted into maximum indexes:
wherein x'ijThe index data after the forward transformation;
the very large index is converted into the positive direction: x'ij＝xij；
The ultra-small index is forward: x'ij＝max{x1j，...，xnj}-xij；
Normalizing intermediate indexes: let the best value in the intermediate index sequence be xbestThe forward formulation is as follows:
Figure RE-GDA0002368121170000111
wherein M is the maximum distance between the index data and the optimal value.
Forward regional indexes: assuming that the optimal interval in the interval type index sequence is [ a, b ], the forward formulation is as follows:
M＝max{a-min{x1j，...，xnj}，max{x1j，...，xnj}-b}，
Figure RE-GDA0002368121170000112
x′ij＝1，(a≤xij≤b)；
Figure RE-GDA0002368121170000113
wherein M is the maximum distance between the index data and the optimal interval.
And (3) normalizing the indexes after the normalization:
Figure RE-GDA0002368121170000114
then x ″)ijAnd index data normalized for the jth index of the ith subject under the label Y.
And (3) normalizing the normalized indexes:
Figure RE-GDA0002368121170000115
wherein p isijThe index data after normalization processing.
According to the normalized index data, calculating the entropy value of the jth index:
Figure RE-GDA0002368121170000121
calculating the weight of each index:
Figure RE-GDA0002368121170000122
wherein d isjFor entropy redundancy of information, wjIs the weight of the index.
And calculating corresponding label values according to the weight of the indexes and the index data, wherein the related formula is as follows:
Figure RE-GDA0002368121170000123
wherein s isiIs the tag value.
The setting method of the classification value is as follows:
for label Y, the label values of n subjects in the standard sample are arranged in descending order, and the sequence is taken as [0.1n ]]Tag value s offirstAs the first division value, take the sequence as [0.9n ]]Tag value s ofsecondAs a second discrimination value.
The index value generation method of the market participating subject comprises the following steps:
let d be the number of market entities participating in the evaluation, and x be m indexes corresponding to the label YijThe index data of the jth index corresponding to the ith main body under the label Y in the reference, i is less than or equal to d, i belongs to z*；j≤m，j∈z*
hij＝xij*wj
Wherein h isijAnd evaluating the index value of the jth index of the market subject i under the label Y. w is ajIs the weight of the jth index under the label Y.
The tag value generation method of the main body participating in the evaluation market comprises the following steps:
Figure RE-GDA0002368121170000124
wherein s isiIs the value of the label of the subject i participating in the market under label Y.
If the tag value si≥sfirstThen the ginseng is evaluated in the marketThe field agent i gives a corresponding first class label, e.g. high liveness, risk pursuit type, low credit risk, etc.
Tag value si∈(sfirst,second) Then the participating market entity i is given a corresponding second grade label, e.g., liveness medium, robust, credit risk, etc.
Tag value si≤ssecondThen the main body i gives the corresponding second grade label, such as low liveness, aversion to risk, high credit risk, etc.
In an optional embodiment, an actually concerned index may be selected according to actual needs, and the target subject may be depicted.
And the output module is connected with the processing module and used for receiving the label and the label value and outputting the label and the label value corresponding to the electric power market main body. The combination of features displayed by the labels represents the overall features of the power market body, i.e. the customer figures that make up the market body.
Taking the main body of the electric power market as an example of a power plant, the electric power market has been marked with labels of high file normative, aversion to risks, sensitive electricity price, weak management capacity, strong operation capacity, strong profit capacity, medium growth capacity, low implementation will, medium social credit, low credit risk, medium liveness, strong prediction capacity and good transaction normative. The displayed characteristics of all the labels of the power plant are integrated to display the overall characteristics of the power plant in the attention dimension of the power trading center, namely the overall image of the power plant in the attention dimension of the power trading center, namely the customer portrait of each market subject.
The invention collects five basic information of basic characteristics, operation level, financial risk, comprehensive credit and transaction capability of the electric power market main body. Indexes are searched from the five basic information, and labels for representing the characteristics of the target object are determined according to index data. The plurality of tags are combined to form a three-dimensional portrait of the market body, thereby realizing effective utilization of data information of the market body.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (7)

1. A label model adapted for customer portrayal of a power market subject, said power market subject comprising a power selling company, a customer and a power plant, comprising:
the system comprises an information input module, a data processing module and a data processing module, wherein the information input module is used for inputting basic information of an electric power market main body, and the basic information comprises at least one of basic characteristics, operation level, financial risk, comprehensive information and transaction capacity data of the electric power market main body;
the processing module is connected with the information input module and used for receiving the basic information and selecting a standard sample, setting a primary index, a secondary index or a special index for the basic information, setting a weight for the primary index, the secondary index or the special index according to the standard sample, and calculating an index value according to a preset model; calculating a label value according to a preset model, setting a classification value of the index according to the preset model, and determining a label reflecting the main characteristics of the power market according to the classification value;
and the output module is connected with the processing module and used for receiving the label and the label value and outputting the label and the label value corresponding to the electric power market main body.
2. The client representation model of claim 1, wherein the primary index is extracted directly from the base information, the secondary index is calculated from the primary index data, the special index is a specific value, and the primary index, the secondary index, and the special index each comprise an index name, index data, and an index value.
3. The client representation model of claim 1 or 2, wherein the calculation of the indicator value is a product of the indicator weight and the indicator data.
4. The client representation model of claim 1 or 2, wherein the tag value is calculated as a sum of index values corresponding to the tag.
5. The client representation model of claim 2, wherein the setting of the weights comprises:
and taking the index weight and the label classification distinguishing value which are obtained by calculating the standard sample in the first evaluation period as the index weight and the label classification distinguishing value of the five future evaluation periods including the evaluation period, and updating the standard sample by using a random number generation function every five evaluation periods.
6. The client representation model of claim 5, wherein the weighting of the indices is computed by entropy weighting:
dividing the indexes into an ultra-large index, an ultra-small index, an intermediate index and an interval index, carrying out forward processing on all the indexes, carrying out standardization processing on the indexes subjected to forward processing, then carrying out normalization processing on the indexes subjected to standardization processing, calculating the entropy values of the indexes according to the indexes subjected to normalization processing, and calculating the weights of all the indexes according to the entropy values.
7. The client representation model of claim 6, wherein the classifier values are set to:
and performing descending arrangement on the label values of the market main bodies in the standard sample, respectively finding out the label value of an integer part with the serial number being 0.1 times and the label value of an integer part with the serial number being 0.9 times of the number of the market main bodies in the standard sample in the descending arrangement, taking the label value of the integer part with the serial number being 0.1 times as a first distinguishing value, and taking the label value of the integer part with the serial number being 0.9 times as a second distinguishing value.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115797044A (en) * 2022-11-15 2023-03-14 东方微银科技股份有限公司 Credit wind control early warning method and system based on cluster analysis

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115797044A (en) * 2022-11-15 2023-03-14 东方微银科技股份有限公司 Credit wind control early warning method and system based on cluster analysis
CN115797044B (en) * 2022-11-15 2024-03-29 东方微银科技股份有限公司 Credit wind control early warning method and system based on cluster analysis

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Application publication date: 20200417