CN109165763B - Method and device for evaluating potential complaints of power grid customer service work order - Google Patents
Method and device for evaluating potential complaints of power grid customer service work order Download PDFInfo
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Abstract
The invention discloses a method and a device for evaluating potential complaints of a power grid customer service work order, which relate to the technical field of power customer service work evaluation and comprise the following steps: firstly, selecting a work order label and a user label; matching according to the work order label and the customer name corresponding to the customer service work order data of the power grid to obtain a comprehensive information label system of the customer service work order of the power grid; constructing a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system; optimizing the regression coefficient by using a user complaint behavior analysis model based on the power grid customer service work order data to obtain an optimal regression coefficient, constructing a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficient, grading and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judging the probability of generating subsequent complaints according to the grading value and the grading grade obtained by grading.
Description
Technical Field
The invention relates to the technical field of power customer service work evaluation, in particular to a method and a device for evaluating potential complaints of a power grid customer service work order.
Background
With the increase of the number of service telephone traffic of the power grid of the power company, service data is increased in number in a guarantee manner, and the complaint pressure of customers is increased day by day. However, currently, a certain problem still exists in the analysis of customer service operation of most power grid companies: the intelligent analysis means is insufficient, and the influence of artificial subjective factors is more; the analysis of internal and external factors of customer service analysis is insufficient, the influence degree and duration cannot be quantized, and further complaint behaviors of customers cannot be predicted so as to adjust differentiated customer service measures in time. The invention provides a method and a device for evaluating potential complaints of a power grid customer service work order, which aim to improve the limitation of current customer service analysis and prediction, improve the level of analysis and prediction, reasonably predict the complaint behaviors of customers in time according to the complaint contents of the customers, improve the satisfaction degree of customer service by using differentiated services and reduce the further complaint behaviors of power customers.
Disclosure of Invention
The invention aims to provide a method and a device for evaluating potential complaints of a power grid customer service work order, so that the defect that a power supply company cannot objectively predict the complaint behaviors of customers according to power grid customer service work order data is overcome.
In order to achieve the purpose, the invention provides a method for evaluating potential complaints of a power grid customer service work order, which comprises the following steps:
s10, selecting a work order label capable of reflecting the electricity utilization behavior of the user from the power grid customer service work order data;
s20, selecting a user label capable of reflecting the electricity utilization behavior of a user from an electricity user database of the electricity marketing management information system;
s30, matching the work order label and the user label with a customer name corresponding to the power grid customer service work order data to obtain a power grid customer service work order comprehensive information label system;
s40, constructing a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system;
s50, optimizing the regression coefficient to obtain an optimal regression coefficient based on the user complaint behavior analysis model of the power grid customer service work order data, constructing the user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficient, grading and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judging the probability of generating subsequent complaints according to the grading value and the grading grade obtained by grading.
Further, the work order label includes: the work order business category, the work order secondary category, the work order tertiary category, the electricity utilization season, the electricity utilization weather, the electricity utilization current local temperature and the work order processing time length.
Further, the user tag includes: the system comprises an address block area, a voltage class, a power utilization type, a line name, a station area name and a network access time.
Further, obtaining a complaint score L of the power grid customer service work order v according to the user complaint behavior analysis model of the power grid customer service work order data:
in the formula (1), L is a complaint grading value of a power grid customer service work order v, and M is a grading factor set; ε is the independent deviation amount; xjThe evaluation is the evaluation of the rating factor j of the power grid customer service work order v; beta is ajThe regression coefficient corresponding to the rating factor j is smoothed by a spline curve, and the regression coefficient is calculated as follows:
in the formulae (2) and (3),
is a basis representing a linear spline function, k is the number of segments of the linear spline function,
is the function value, beta, of the rightmost point in the range of the linear spline functionj1And betajKIs a linear spline functionThe value of k-s at the left and right ends of the interval3Is the piecewise function minus a constant, s, less than 13Is [0,1 ]]A constant of (k-s)KβIs the number of segments minus a constant, s, smaller than 1KβIs [0,1 ]]Is constant.
Further, the calculation process of the optimal regression coefficient is as follows: obtaining a rating stability objective function of the user complaint behaviors of the power grid customer service work order data and a rating difference objective function of the user complaint behaviors of the power grid customer service work order data through the user complaint behavior analysis model of the power grid customer service work order data; forming a multi-objective constraint optimization problem according to the rating stability objective function and the rating difference objective function; and converting the multi-objective constraint optimization problem into a single-objective optimization problem through an optimization algorithm, thereby obtaining an optimal regression coefficient.
Further, the rating stability objective function is as follows:
in the formula (4), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for the work order, N is the number of work orders that need to be evaluated,
represents the average of N numbers, L is the complaint score value of the power grid customer service work order v,
the expected value of the potential complaints of the power grid customer service work order v is obtained;
the rating variance objective function is as follows:
in the formula (5), the reaction mixture is,
is the difference value of the ranking of the potential complaints of the work order, N is the number of work orders to be evaluated,
the average number of N-1 is shown, L is the complaint score value of the power grid customer service work order v,
is the average value of potential complaint possibility of a power grid customer service work order v,
is an average value
Square of (d);
the multi-objective constraint optimization problem is as follows:
in the formula (6), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for work orders,
and beta is the optimal regression coefficient for the difference value of the ranking of the potential complaints of the work order.
An evaluation device of potential complaints of a grid customer service work order, comprising:
the work order tag module is used for selecting a work order tag capable of reflecting the power utilization behavior of the user from the power grid customer service work order data;
the user tag module is used for selecting a user tag capable of reflecting the electricity utilization behavior of a user from an electricity user database of the electricity marketing management information system;
the system construction module is used for matching the work order label and the user label with the customer name corresponding to the power grid customer service work order data to obtain a power grid customer service work order comprehensive information label system;
the model building module is used for building a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system; and
and the optimization model construction module is used for optimizing regression coefficients according to the user complaint behavior analysis model of the power grid customer service work order data to obtain optimal regression coefficients, constructing a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficients, grading and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judging the probability of generating subsequent complaints according to the grading values and the grading levels obtained by grading.
Compared with the prior art, the invention has the following beneficial effects:
according to the method and the device for evaluating the potential complaints of the power grid customer service work order, the work order label capable of reflecting the power utilization behavior of the user is selected through the power grid customer service work order data; selecting a user label capable of reflecting the power utilization behavior of a user from a power user database of a power marketing management information system; matching the work order label and the customer name corresponding to the power grid customer service work order data with a work order label to obtain a power grid customer service work order comprehensive information label system, wherein the system comprises various labels and can describe the behavior characteristics of power customers in multiple directions; then, a user complaint behavior analysis model of the power grid customer service work order data is constructed according to the power grid customer service work order comprehensive information label system; the model is more accurate by optimizing the regression coefficient, the user complaint behavior analysis model of the optimal power grid customer service order data is obtained based on the optimized regression coefficient, the power grid customer service order is graded and graded by the user complaint behavior analysis model of the optimal power grid customer service order data, and the probability of the subsequent complaint behavior generated by the power grid customer service order is predicted by grading, so that the probability of the subsequent complaint behavior generated by service personnel of a power company according to different power grid customer service orders is effectively assisted, the differentiated value-added service is provided, the service level of the power customer is improved, the probability of repeated complaints of the power grid customer service center of the power supply company is further reduced, and the possibility of further dialing 12398 electric supervision complaint telephone numbers by the power customer is reduced.
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 are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment 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 flow chart of a method of the present invention for assessing potential complaints about a grid customer service work order;
FIG. 2 is a schematic structural diagram of a potential complaint evaluation device for a power grid customer service work order according to the present invention;
wherein: 101-work order tag module, 102-user tag module, 103-system building module, 104-model building module and 105-optimization model building module.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for evaluating potential complaints of a grid customer service work order provided by the present invention includes the following steps:
s10, selecting a work order label capable of reflecting the electricity utilization behavior of the user from the power grid customer service work order data, wherein the work order label comprises: work order business category, work order secondary category, work order tertiary category, electricity season, electricity weather, electricity current local temperature (centigrade) and work order processing duration (minute).
The work order service categories include: electricity usage, consultation queries, subscription information, troubleshooting, others, advice, praise, complaints, reports, and opinions.
The work order secondary categories include: work order solicitation, appeal cancellation, general appeal work order, inquiry power cut and delivery, reading check and receipt, electric energy metering, power grid construction and safety, service channels, client basic information inquiry, arrearage power failure, business expansion business, subscription information, subscription cancellation, low-voltage power failure, voltage abnormity, high-voltage power failure, power supply facility fault, other emergency repair business, return visit client, internal contact solicitation, internal business communication, other, power grid construction, power supply safety and client service, other recommendations, new energy, business operations, service attitudes, service efficiencies, others, service levels, meter reading and billing, power metering, grid construction, service complaints, power supply security, power supply quality, outage rush repairs, business expansion gear, other reports, electricity stealing, default (supply) power, grid construction, power supply security, power supply quality, return comments, customer services, power outage comments, and business operations.
The three classes of work orders include: business network consultation, call forwarding/call error making by a user, no need of processing by contacting clients, successful return visit of the clients, subsequent processing by contacting clients, electricity price and electricity charge, voltage quality, return visit of business expansion worksheets, new installation capacity increasing, power supply facilities, power supply reliability, subsequent processing by contacting clients, electric energy metering and electricity utilization change.
The summary of the work order service class, the work order secondary class, and the work order tertiary class is shown in table 1.
Table 1: summary of work order business category, work order secondary category and work order tertiary category
S20, selecting a user label capable of reflecting the electricity utilization behavior of the user from the electricity user database of the electricity marketing management information system, wherein the user label comprises: address tile, voltage class (volt), electricity class, line name, station name, and duration of network entry (year).
And S30, matching the work order label and the user label with the customer name corresponding to the power grid customer service work order data to obtain a power grid customer service work order comprehensive information label system. The comprehensive information label system table of the power grid customer service work order is shown in table 2.
Table 2: comprehensive information label system table for power grid customer service work order
S40, constructing a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system, and obtaining a complaint score L of the power grid customer service work order v according to the user complaint behavior analysis model of the power grid customer service work order data:
in the formula (1), L is a complaint grading value of a power grid customer service work order v, and M is a grading factor set; ε is the independent deviation amount; xjThe evaluation is the evaluation of the rating factor j of the power grid customer service work order v; beta is ajThe regression coefficient corresponding to the rating factor j is smoothed by a spline curve, and the regression coefficient is calculated as follows:
in the formulae (2) and (3),
is a basis representing a linear spline function, k is the number of segments of the linear spline function,
is the function value, beta, of the rightmost point in the range of the linear spline functionj1And betajKIs the value of the left and right ends of the interval where the linear spline function is located, k-s3Is the piecewise function minus a constant, s, less than 13Is [0,1 ]]A constant of (k-s)KβIs the number of segments minus a constant, s, smaller than 1KβIs [0,1 ]]Is constant.
S50, optimizing the regression coefficient based on the user complaint behavior analysis model of the power grid customer service work order data to obtain an optimal regression coefficient, wherein the optimal regression coefficient is calculated as follows:
s501, obtaining a rating stability objective function of the user complaint behaviors of the power grid customer service work order data according to the user complaint behavior analysis model of the power grid customer service work order data, wherein the rating stability objective function is as follows:
in the formula (4), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for the work order, N is the number of work orders that need to be evaluated,
represents the average of N numbers, L is the complaint score value of the power grid customer service work order v,
the expected value of the potential complaints of the power grid customer service work order v is obtained;
s502, obtaining a rating difference target function of the user complaint behaviors of the power grid customer service work order data according to the user complaint behavior analysis model of the power grid customer service work order data, wherein the rating difference target function is as follows:
in the formula (5), the reaction mixture is,
is the difference value of the ranking of the potential complaints of the work order, N is the number of work orders to be evaluated,
the average number of N-1 is shown, L is the complaint score value of the power grid customer service work order v,
is the average value of potential complaint possibility of a power grid customer service work order v,
is an average value
Square of (d);
s503, forming a multi-target constraint optimization problem according to the rating stability objective function and the rating difference objective function, wherein the multi-target constraint optimization problem is as follows:
in the formula (6), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for work orders,
the difference value of the potential complaint possibility grades of the work order is shown, and beta is an optimal regression coefficient;
s504, converting the multi-objective constraint optimization problem into a single-objective optimization problem through an optimization algorithm, and obtaining an optimal regression coefficient beta;
constructing a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficient, enabling the user complaint behavior analysis model of the optimal power grid customer service work order data to be more accurate, and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data to obtain a real-time grading value; the user complaint behavior analysis model of the optimal power grid customer service work order data classifies the grade values in advance, and the grade values are classified into four grades from high to low according to the grade values: the probability that the customers can further complain is increased in sequence, wherein each grade corresponds to the probability that the customers can further complain, and the probabilities that the customers corresponding to the worksheets corresponding to the four grades of the very satisfactory grade, the relatively satisfactory grade, the substantially satisfactory grade and the unsatisfactory grade correspond to the worksheets; the user complaint behavior analysis model of the optimal power grid customer service work order data divides the real-time score value into the grade, and the probability of generating subsequent complaints, namely the possibility of generating the subsequent complaints by the user, can be determined through the divided grade.
As shown in fig. 2, the present invention provides an evaluation apparatus for potential complaints of a power grid customer service work order, including: a work order label module 101, a user label module 102, a system construction module 103, a model construction module 104 and an optimization model construction module 105. When evaluating potential complaints of a power grid customer service work order, firstly, a work order label module 101 selects a work order label capable of reflecting user electricity utilization behaviors from power grid customer service work order data, and a user label module 102 selects a user label capable of reflecting the user electricity utilization behaviors from a power user database of a power marketing management information system; secondly, the system construction module 103 matches the customer name corresponding to the power grid customer service work order data according to the work order label and the user label to obtain a power grid customer service work order comprehensive information label system; then, the model construction module 104 constructs a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system; finally, the optimization model construction module 105 optimizes the regression coefficient according to the user complaint behavior analysis model of the power grid customer service work order data to obtain an optimal regression coefficient, constructs a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficient, scores and grades the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judges the probability of generating subsequent complaints according to the score value and the grade obtained by scoring. Specifically, please refer to the content of the corresponding method embodiment for the specific working content of each module in this embodiment, which is not described herein again.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (5)
1. A method for evaluating potential complaints of a power grid customer service work order is characterized by comprising the following steps: the method comprises the following steps:
s10, selecting a work order label capable of reflecting the electricity utilization behavior of the user from the power grid customer service work order data;
s20, selecting a user label capable of reflecting the electricity utilization behavior of a user from an electricity user database of the electricity marketing management information system;
s30, matching the work order label and the user label with a customer name corresponding to the power grid customer service work order data to obtain a power grid customer service work order comprehensive information label system;
s40, constructing a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system;
s50, optimizing a regression coefficient based on the user complaint behavior analysis model of the power grid customer service work order data to obtain an optimal regression coefficient, constructing a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficient, grading and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judging the probability of generating subsequent complaints according to the grading value and the grading grade obtained by grading;
the calculation process of the optimal regression coefficient is as follows: obtaining a rating stability objective function of the user complaint behaviors of the power grid customer service work order data and a rating difference objective function of the user complaint behaviors of the power grid customer service work order data through the user complaint behavior analysis model of the power grid customer service work order data; forming a multi-objective constraint optimization problem according to the rating stability objective function and the rating difference objective function; converting the multi-objective constraint optimization problem into a single-objective optimization problem through an optimization algorithm, so as to obtain an optimal regression coefficient;
the rating stability objective function is as follows:
in the formula (4), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for the work order, N is the number of work orders that need to be evaluated,
represents the average of N numbers, L is the complaint score value of the power grid customer service work order v,
the expected value of the potential complaints of the power grid customer service work order v is obtained;
the rating variance objective function is as follows:
in the formula (5), the reaction mixture is,
is the difference value of the ranking of the potential complaints of the work order, N is the number of work orders to be evaluated,
the average number of N-1 is shown, L is the complaint score value of the power grid customer service work order v,
is the average value of potential complaint possibility of a power grid customer service work order v,
is an average value
Square of (d);
the multi-objective constraint optimization problem is as follows:
in the formula (6), the reaction mixture is,
a rating stable value for rating the likelihood of potential complaints for work orders,
and beta is the optimal regression coefficient for the difference value of the ranking of the potential complaints of the work order. .
2. The method of evaluating a potential complaint of a grid customer service work order of claim 1, wherein: the work order label includes: the work order business category, the work order secondary category, the work order tertiary category, the electricity utilization season, the electricity utilization weather, the electricity utilization current local temperature and the work order processing time length.
3. The method of evaluating a potential complaint of a grid customer service work order of claim 1, wherein: the user tag includes: the system comprises an address block area, a voltage class, a power utilization type, a line name, a station area name and a network access time.
4. The method of evaluating a potential complaint of a grid customer service work order of claim 1, wherein: obtaining a complaint score L of the power grid customer service work order v according to the user complaint behavior analysis model of the power grid customer service work order data:
in the formula (1), L is a complaint grading value of a power grid customer service work order v, and M is a grading factor set; ε is the independent deviation amount; xjThe evaluation is the evaluation of the rating factor j of the power grid customer service work order v; beta is ajThe regression coefficient corresponding to the rating factor j is smoothed by a spline curve, and the regression coefficient is calculated as follows:
in the formulae (2) and (3),
is a basis representing a linear spline function, k is the number of segments of the linear spline function,
is the function value, beta, of the rightmost point in the range of the linear spline functionj1And betajKIs the value of the left and right ends of the interval where the linear spline function is located, k-s3Is the piecewise function minus a constant, s, less than 13Is [0,1 ]]A constant of (k-s)KβIs the number of segments minus a constant, s, smaller than 1KβIs [0,1 ]]Is constant.
5. An evaluation device of potential complaints of a power grid customer service work order, which applies the evaluation method of potential complaints of a power grid customer service work order of claim 1, characterized in that: the method comprises the following steps:
the work order tag module is used for selecting a work order tag capable of reflecting the power utilization behavior of the user from the power grid customer service work order data;
the user tag module is used for selecting a user tag capable of reflecting the electricity utilization behavior of a user from an electricity user database of the electricity marketing management information system;
the system construction module is used for matching the work order label and the user label with the customer name corresponding to the power grid customer service work order data to obtain a power grid customer service work order comprehensive information label system;
the model building module is used for building a user complaint behavior analysis model of the power grid customer service work order data according to the power grid customer service work order comprehensive information label system; and
and the optimization model construction module is used for optimizing regression coefficients according to the user complaint behavior analysis model of the power grid customer service work order data to obtain optimal regression coefficients, constructing a user complaint behavior analysis model of the optimal power grid customer service work order data based on the optimal regression coefficients, grading and grading the power grid customer service work order by using the user complaint behavior analysis model of the optimal power grid customer service work order data, and judging the probability of generating subsequent complaints according to the grading values and the grading levels obtained by grading.
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| CN110414819B (en) * | 2019-07-19 | 2023-05-26 | 中国电信集团工会上海市委员会 | Work order scoring method |
| CN111126783A (en) * | 2019-11-29 | 2020-05-08 | 广东电网有限责任公司 | Customer complaint risk rating method and device based on big data |
| CN111178957B (en) * | 2019-12-23 | 2023-04-14 | 广西电网有限责任公司 | Method for early warning sudden increase of electric quantity of electricity consumption customer |
| CN111489095B (en) * | 2020-04-15 | 2023-07-25 | 腾讯科技（深圳）有限公司 | Risk user management method, apparatus, computer device and storage medium |
| CN111353792A (en) * | 2020-05-25 | 2020-06-30 | 广东电网有限责任公司惠州供电局 | Client portrait system with visual display and data analysis functions |
| CN112446615B (en) * | 2020-11-26 | 2023-10-20 | 深圳供电局有限公司 | Uninterrupted operation priority evaluation method |
| CN112529630A (en) * | 2020-12-17 | 2021-03-19 | 国网河北省电力有限公司营销服务中心 | Construction method of multi-dimensional informatization label system of power consumer |
| CN112991049B (en) * | 2021-04-13 | 2023-05-30 | 重庆度小满优扬科技有限公司 | Loan information processing method and electronic equipment |
| CN113254644B (en) * | 2021-06-07 | 2021-09-17 | 成都数之联科技有限公司 | Model training method, non-complaint work order processing method, system, device and medium |
| CN114240225A (en) * | 2021-12-23 | 2022-03-25 | 国网山东省电力公司东营供电公司 | Power system operation evaluation method and system under source network load storage multivariate cooperative interaction |
| CN115481925A (en) * | 2022-10-12 | 2022-12-16 | 中邮消费金融有限公司 | Customer service early warning method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104680428A (en) * | 2015-03-16 | 2015-06-03 | 朗新科技股份有限公司 | Construction method of power grid customer satisfaction model |
| CN107240033A (en) * | 2017-06-07 | 2017-10-10 | 国家电网公司客户服务中心 | The construction method and system of a kind of electric power identification model |
| CN107437124A (en) * | 2017-07-20 | 2017-12-05 | 大连大学 | A kind of operator based on big data analysis complains and trouble correlation analytic method |
| CN107992609A (en) * | 2017-12-15 | 2018-05-04 | 广东电网有限责任公司信息中心 | A kind of complaint tendency determination methods based on Text Classification and decision tree |
-
2018
- 2018-06-13 CN CN201810610029.XA patent/CN109165763B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104680428A (en) * | 2015-03-16 | 2015-06-03 | 朗新科技股份有限公司 | Construction method of power grid customer satisfaction model |
| CN107240033A (en) * | 2017-06-07 | 2017-10-10 | 国家电网公司客户服务中心 | The construction method and system of a kind of electric power identification model |
| CN107437124A (en) * | 2017-07-20 | 2017-12-05 | 大连大学 | A kind of operator based on big data analysis complains and trouble correlation analytic method |
| CN107992609A (en) * | 2017-12-15 | 2018-05-04 | 广东电网有限责任公司信息中心 | A kind of complaint tendency determination methods based on Text Classification and decision tree |
Non-Patent Citations (1)
| Title |
|---|
| Intelligent customer complaint handling utilising principal component and data envelopment analysis;Alireza Faed .etc;《Applied Soft Computing》;20150314;614-630 * |
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