CN115456210B - Power consumption complaint early warning method based on cascading logistic regression Bayesian algorithm - Google Patents
Power consumption complaint early warning method based on cascading logistic regression Bayesian algorithm Download PDFInfo
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Abstract
The invention provides a power consumption complaint early warning method based on a cascading logistic regression Bayesian algorithm, which comprises the following steps: acquiring the power failure time of a power grid and the complaint time of a user, calculating a power failure forgetting index of the user, and screening out the complaint information of the user in the effective complaint range according to the power failure forgetting index; acquiring fault basic information and fault associated information corresponding to the screened user complaint information, extracting basic features based on the fault basic information and the user complaint information, and extracting additional features based on the fault associated information; taking the blackout forgetting index, the basic features and the additional features as final feature sets, and respectively inputting the final feature sets into a trained logistic regression model and a naive Bayesian model; and cascading the results output by the logistic regression model and the naive Bayesian model to obtain the result of early warning of electricity consumption complaints. The invention realizes screening of potential complaint users, and more accurate early warning of electricity complaint risks, and can effectively reduce the workload of basic staff.
Description
Technical Field
The invention belongs to the field of big data analysis, and particularly relates to a power consumption complaint early warning method based on a cascading logistic regression Bayesian algorithm.
Background
When the power grid enterprise provides power service for users, if facilities such as a power distribution network and a power transmission line are failed, the complaints of the users who use the power are likely to be caused, and for the power grid enterprise, how to reduce the complaints of the users in the power utilization process is important content in the work of improving the power supply quality. At present, power supply enterprises constantly optimize and optimize power supply capacity through canceling power outage operation and popularizing live working, improve power supply reliability, and early warn the risk of power consumption complaints in advance through simple rules aiming at fault conditions, and then manually inform users affected by power outage so as to improve customer power consumption satisfaction.
However, due to the unavoidable conditions of equipment fault and power failure overhaul in the power transmission and distribution process, for the areas with wider power distribution scale and higher power supply quality requirements, the early warning range of power grid enterprises on the power consumption complaint risk is too large, the first power consumption complaint risk of users is difficult to accurately early warn, the workload of primary staff is huge, and the customer satisfaction degree is very limited.
Disclosure of Invention
In order to solve the problems that a power grid enterprise is difficult to accurately early warn the electricity complaint risk of a user and the workload of primary staff is large, the invention provides an electricity complaint early warning method based on a cascading logistic regression Bayesian algorithm, through learning historical data, preliminary screening is carried out on complaint information according to timeliness of complaints, and in combination with external factor data, a logistic regression model and a naive Bayesian algorithm are applied to carry out cascading analysis, so that a user range with large electricity complaint risk is searched, and the power grid enterprise can communicate with the searched user through telephone, short messages and the like after power failure, thereby reducing complaint occurrence and improving customer satisfaction.
The invention provides a power consumption complaint early warning method based on a cascading logistic regression Bayesian algorithm, which comprises the following steps:
acquiring the power failure time of a power grid and the complaint time of a user, calculating a power failure forgetting index of the user, and screening out the complaint information of the user in the effective complaint range according to the power failure forgetting index;
acquiring fault basic information and fault associated information corresponding to the screened user complaint information, extracting basic features based on the fault basic information and the user complaint information, and extracting additional features based on the fault associated information;
taking the blackout forgetting index, the basic features and the additional features as final feature sets, and respectively inputting the final feature sets into a trained logistic regression model and a naive Bayesian model;
and cascading the results output by the logistic regression model and the naive Bayesian model to obtain the result of early warning of electricity consumption complaints.
Optionally, the acquiring the outage time of the power grid and the complaint time of the user, calculating the outage forgetting index of the user, and screening the electricity complaint information in the effective complaint range according to the outage forgetting index, including:
acquiring power failure time corresponding to a power grid failure, and respectively calculating time difference t between complaint time of a user i and the power failure time of the power grid;
calculating a user blackout forgetting index T=Σe according to the time difference T -t/α Alpha is a preset unit conversion coefficient;
judging whether the power failure forgetting index of the user is in a preset range or not, if so, screening out a user i corresponding to the power failure forgetting index of the user, and judging that the power consumption complaint information of the user i is in an effective complaint range.
Optionally, the obtaining the fault basic information and the fault associated information corresponding to the screened electricity complaint information includes: acquiring a distribution transformer power outage list in an SQL database, and acquiring a station area corresponding to a power outage line in the distribution transformer power outage list;
the user name in the electricity complaint information is used as a key, the platform area is matched with the user name, and fault section data of the matched platform area is used as fault basic information;
and taking the electric quantity information and weather information corresponding to the matched area as fault associated information.
Optionally, after the matching the area with the user name, the method further includes:
and deleting the repeated user names and the electricity complaint information corresponding to the user names in the user names matched with the same platform area.
Optionally, the basic features extracted based on the fault basic information include a fault line name, a fault determination time, a fault recovery time, and a fault date property.
Optionally, the basic features extracted based on the electricity complaint information include complaint time, complaint content, user number, user classification and household duration.
Optionally, the additional features include impact time, user blackout forgetfulness index, user's last month peak valley power, user's last month day power, weather of the day of failure, and air temperature.
Optionally, the process of calculating the influence time includes:
acquiring fault judging time and fault recovering time in the basic fault information and complaint time in the electricity complaint information; and respectively calculating the difference value between the complaint time and the fault studying and judging time and the difference value between the fault recovery time and the fault studying and judging time, and taking the minimum value in the difference value as the influence time.
Optionally, the cascading the results output by the logistic regression model and the naive bayes model to obtain the result of early warning of electricity complaints includes:
and taking an intersection of output results of the logistic regression model and the naive Bayesian model, and generating electricity consumption complaint risk early warning aiming at users in the intersection.
The technical scheme provided by the invention has the beneficial effects that:
according to the technical scheme provided by the invention, through learning analysis on information related to faults and user complaints, through learning of historical data, preliminary screening is carried out on complaint information according to timeliness of complaints, and data volumes of two learning models of input logistic regression and Bayesian algorithm are reduced. In addition, two learning models of logistic regression and Bayesian algorithm are adopted to realize screening of potential complaint users, so that effective communication is realized for the users, and satisfaction of customer work is improved.
Compared with a conventional working method, the technical scheme realizes more accurate early warning on the risk of electricity complaints, so that the workload of basic staff can be effectively reduced to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an electricity complaint early warning method based on a cascading logistic regression Bayesian algorithm according to an embodiment of the present invention;
fig. 2 is a schematic diagram of correspondence between user complaint information, fault basic information and fault associated information according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present invention, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponding to B", or "B corresponding to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also determine B from a and/or other information. The matching of A and B is that the similarity of A and B is larger than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection" depending on the context.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Examples:
as shown in fig. 1, this embodiment proposes a power consumption complaint early warning method based on a cascaded logistic regression bayesian algorithm, including:
s1: acquiring the power failure time of a power grid and the complaint time of a user, calculating the power failure forgetting index of the user, and screening out the power consumption complaint information in the effective complaint range according to the power failure forgetting index;
s2: acquiring the screened fault basic information and fault associated information corresponding to the electricity complaint information, extracting basic characteristics based on the fault basic information and the electricity complaint information, and extracting additional characteristics based on the fault basic information, the fault associated information and the electricity complaint information;
s3: taking the blackout forgetting index, the basic features and the additional features as final feature sets, and respectively inputting the final feature sets into a trained logistic regression model and a naive Bayesian model;
s4: and cascading the results output by the logistic regression model and the naive Bayesian model to obtain the result of early warning of electricity consumption complaints.
The embodiment aims to accurately judge the electricity complaint risk of the user and reduce the workload of first-line staff of the base layer. The method comprises the steps of obtaining electricity consumption complaint information, and related fault basic information and fault related information thereof, realizing preliminary screening of the electricity consumption complaint information combined with external factor data, then applying a cascading algorithm of a logistic regression model and a naive Bayesian model to search a range of potential electricity consumption complaint users, and communicating the checked users through telephone, short messages and the like after power failure so as to reduce occurrence of complaints and improve customer satisfaction.
In order to preliminarily screen the electricity consumption complaint information in the effective complaint range, avoid the interference of invalid information with longer complaint time, in this embodiment, obtain the outage time of the electric wire netting and the complaint time of the user, calculate the user outage forgetting index, screen the electricity consumption complaint information in the effective complaint range according to the outage forgetting index, include:
acquiring power failure time corresponding to a power grid failure, and respectively calculating time difference t between complaint time of a user i and the power failure time of the power grid;
calculating a user blackout forgetting index T=Σe according to the time difference T -t/α Alpha is presetA unit conversion coefficient;
judging whether the power failure forgetting index of the user is in a preset range or not, if so, screening out a user i corresponding to the power failure forgetting index of the user, and judging that the power consumption complaint information of the user i is in an effective complaint range.
Specifically, in this embodiment, T is expressed in seconds, and α is set to 3600/24/10, that is, in this embodiment, the user blackout forgetting index t=Σe -t/3600/24/10 。
In this embodiment, the preset range is a range greater than a preset index threshold, that is, the electricity consumption complaint information of the complaint time after a certain moment is screened out, so that interference of the electricity consumption complaint information of an earlier time to the current early warning analysis is avoided, and preliminary screening of potential complaint users is realized.
In this embodiment, in order to combine the electricity complaint information with other external information to perform early warning analysis, in this embodiment, the obtaining the fault basic information and the fault associated information corresponding to the screened electricity complaint information includes:
acquiring a distribution transformer power outage list in an SQL database, and acquiring a station area corresponding to a power outage line in the distribution transformer power outage list;
the user name in the electricity complaint information is used as a key, the platform area is matched with the user name, and fault section data of the matched platform area is used as fault basic information;
and taking the electric quantity information and weather information corresponding to the matched area as fault associated information.
The corresponding relation between the electricity complaint information, the fault basic information and the fault associated information is shown in fig. 2, and the fault basic information is fault section data, including power failure number, fault judging time, fault recovery time and line name; weather information is taken as one of fault associated information, and comprises date, air temperature and weather conditions; the system is communicated with the system for acquiring other fault care information, and the stored data comprise equipment codes, affiliated lines and platform area codes, and can acquire the electricity consumption conditions of all users in the platform area; the electricity consumption complaint information comprises complaint numbers, complaint time, complaint content and user numbers.
The user number is used as a key, the code of the platform region corresponding to the user number is firstly determined to realize matching, so that the matching of electricity complaint information and marketing and distribution through related information is realized, then the fault section data of the corresponding line name is matched through the line belonging to the matching information, and finally the corresponding weather information is matched through the fault judging time.
In this embodiment, considering that there may be electricity complaint information generated by some users frequently initiating complaints, which affects the early warning analysis efficiency, after matching the platform area with the user name, the method further includes: and deleting the repeated user names and the electricity complaint information corresponding to the user names in the user names matched with the same platform area.
As can be seen from fig. 2, in the present embodiment, the basic features extracted based on the fault basic information include a fault line name, a fault determination time, a fault recovery time, and a fault date property, wherein the fault date property includes a working day, a holiday, and a holiday.
The basic characteristics extracted based on the electricity complaint information comprise complaint time, complaint content, user numbers, user classifications and household time.
The additional features include impact time, user blackout forgetfulness index, user's last month peak valley power, user's last month day power, weather of the day of failure, and air temperature.
The process for calculating the influence time comprises the following steps:
acquiring fault judging time and fault recovering time in the basic fault information and complaint time in the electricity complaint information; and respectively calculating the difference value between the complaint time and the fault studying and judging time and the difference value between the fault recovery time and the fault studying and judging time, and taking the minimum value in the difference value as the influence time.
Finally, the basic features and the additional features are spliced into a final feature set, and are respectively input into a trained logistic regression model and a naive Bayes model. Therefore, in the embodiment, the feature data of the input logistic regression model and the naive Bayesian model are fused with multidimensional features including power failure duration, weather conditions, date attributes, complaint attributes and the like, so that the early warning analysis accuracy of potential complaint users can be further improved.
In this embodiment, the logistic regression model is a conventional classification regression model, which can be implemented by a perceptron, and is used to determine whether the current fault will cause a user complaint, and the level of complaint caused. The naive Bayes model is a conventional classification model based on independent assumption of Bayes theorem and characteristic conditions, and classification is achieved through probability analysis.
In order to improve the accuracy of the early warning result of electricity consumption complaints, the method for cascading the results output by the logistic regression model and the naive bayes model to obtain the early warning result of electricity consumption complaints includes:
and taking an intersection of output results of the logistic regression model and the naive Bayesian model, and generating electricity consumption complaint risk early warning aiming at users in the intersection.
The various numbers in the above embodiments are for illustration only and do not represent the order of assembly or use of the various components.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather, the present invention is to be construed as limited to the appended claims.
Claims (3)
1. The utility model provides a power consumption complaint early warning method based on cascade logistic regression Bayesian algorithm, which is characterized in that the power consumption complaint early warning method comprises the following steps:
acquiring the power failure time of a power grid and the complaint time of a user, calculating the power failure forgetting index of the user, and screening out the power consumption complaint information in the effective complaint range according to the power failure forgetting index;
acquiring the screened fault basic information and fault associated information corresponding to the electricity complaint information, extracting basic characteristics based on the fault basic information and the electricity complaint information, and extracting additional characteristics based on the fault basic information, the fault associated information and the electricity complaint information;
taking the blackout forgetting index, the basic features and the additional features as final feature sets, and respectively inputting the final feature sets into a trained logistic regression model and a naive Bayesian model;
cascading the results output by the logistic regression model and the naive Bayesian model to obtain the result of early warning of electricity complaints;
the method for acquiring the power outage time of the power grid and the complaint time of the user, calculating the power outage forgetting index of the user, and screening the power consumption complaint information in the effective complaint range according to the power outage forgetting index comprises the following steps:
acquiring power failure time corresponding to a power grid failure, and respectively calculating time difference t between complaint time of a user i and the power failure time of the power grid;
calculating a user blackout forgetting index T=Σe according to the time difference T -t/α Alpha is a preset unit conversion coefficient;
judging whether the power failure forgetting index of the user is in a preset range or not, if so, screening out a user i corresponding to the power failure forgetting index of the user, and judging that the power consumption complaint information of the user i is in an effective complaint range;
the obtaining the screened fault basic information and fault associated information corresponding to the electricity complaint information comprises the following steps:
acquiring a distribution transformer power outage list in an SQL database, and acquiring a station area corresponding to a power outage line in the distribution transformer power outage list;
the user name in the electricity complaint information is used as a key, the platform area is matched with the user name, and fault section data of the matched platform area is used as fault basic information;
taking the electric quantity information and weather information corresponding to the matched station area as fault associated information;
the basic characteristics extracted based on the fault basic information comprise fault line names, fault judging time, fault recovery time and fault date properties;
basic features extracted based on the electricity complaint information comprise complaint time, complaint content, user numbers, user classifications and household time;
the additional features include influence time, user blackout forget index, user's last month peak valley power, user's last month day power consumption, weather of the day of failure and air temperature;
the cascade connection of the results output by the logistic regression model and the naive Bayesian model to obtain the result of early warning of electricity complaints comprises the following steps:
and taking an intersection of output results of the logistic regression model and the naive Bayesian model, and generating electricity consumption complaint risk early warning aiming at users in the intersection.
2. The electricity complaint early warning method based on the cascading logistic regression bayesian algorithm according to claim 1, further comprising, after the matching of the area with the user name:
and deleting the repeated user names and the electricity complaint information corresponding to the user names in the user names matched with the same platform area.
3. The electricity consumption complaint early warning method based on the cascading logistic regression bayesian algorithm according to claim 1, wherein the calculation process of the influence time comprises the following steps:
acquiring fault judging time and fault recovering time in the basic fault information and complaint time in the electricity complaint information;
and respectively calculating the difference value between the complaint time and the fault studying and judging time and the difference value between the fault recovery time and the fault studying and judging time, and taking the minimum value in the difference value as the influence time.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101719147A (en) * | 2009-11-23 | 2010-06-02 | 合肥兆尹信息科技有限责任公司 | Rochester model-naive Bayesian model-based data classification system |
| CN109410089A (en) * | 2018-12-29 | 2019-03-01 | 广州供电局有限公司 | Low-voltage tripping and customer complaint prediction technique, device and storage medium |
| CN109447364A (en) * | 2018-11-08 | 2019-03-08 | 国网湖南省电力有限公司 | Power customer based on label complains prediction technique |
| CN111178957A (en) * | 2019-12-23 | 2020-05-19 | 广西电网有限责任公司 | Method for early warning sudden increase of electric quantity of electricity consumption customer |
| CN111723519A (en) * | 2020-05-29 | 2020-09-29 | 国网四川省电力公司电力科学研究院 | Transformer fault diagnosis device and method based on logistic regression and naive Bayes |
| CN111860940A (en) * | 2020-06-02 | 2020-10-30 | 山西潞安环保能源开发股份有限公司 | Coal mine distribution network power failure accident prediction method based on logistic regression algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10713692B2 (en) * | 2017-10-13 | 2020-07-14 | Oath Inc. | Systems and methods for user propensity classification and online auction design |
-
2022
- 2022-08-22 CN CN202211006404.2A patent/CN115456210B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101719147A (en) * | 2009-11-23 | 2010-06-02 | 合肥兆尹信息科技有限责任公司 | Rochester model-naive Bayesian model-based data classification system |
| CN109447364A (en) * | 2018-11-08 | 2019-03-08 | 国网湖南省电力有限公司 | Power customer based on label complains prediction technique |
| CN109410089A (en) * | 2018-12-29 | 2019-03-01 | 广州供电局有限公司 | Low-voltage tripping and customer complaint prediction technique, device and storage medium |
| CN111178957A (en) * | 2019-12-23 | 2020-05-19 | 广西电网有限责任公司 | Method for early warning sudden increase of electric quantity of electricity consumption customer |
| CN111723519A (en) * | 2020-05-29 | 2020-09-29 | 国网四川省电力公司电力科学研究院 | Transformer fault diagnosis device and method based on logistic regression and naive Bayes |
| CN111860940A (en) * | 2020-06-02 | 2020-10-30 | 山西潞安环保能源开发股份有限公司 | Coal mine distribution network power failure accident prediction method based on logistic regression algorithm |
Non-Patent Citations (3)
| Title |
|---|
| Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms;Viet-Ha Nhu等;《Int.J.Environ.Res.Public Health》;20200416;全文 * |
| 机器学习经典算法在无线设备故障预警用户投诉行为的应用;梁景舒;《信息科技》;全文 * |
| 机器学习经典算法在无线设备故障预警用户投诉行为的应用;梁景舒;顾俊峰;李江;黄伟锋;李土光;;中国新通信;20180420(08);全文 * |
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