CN111210057A - Method for predicting complaints of mobile phone internet users - Google Patents
Method for predicting complaints of mobile phone internet users Download PDFInfo
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Abstract
The method for predicting the complaint of the mobile phone internet user comprises a complaint data acquisition module, a data index acquisition module, an application category data acquisition module, a cost and flow index acquisition module, an internet positioning acquisition module and a prediction analysis system; the complaint data acquisition module, the data index acquisition module, the application type data acquisition module, the cost and flow index acquisition module and the internet surfing positioning acquisition module are all connected with the prediction analysis signal and data transmission; and the prediction analysis system is used for calculating and processing the user complaint information, the mobile phone internet network data index, the application category data, the charging system data index and the internet positioning data to generate a complaint characteristic data set, and generating a prediction model by the obtained complaint characteristic data set through a machine learning algorithm. The invention can accurately predict the complaints of the mobile internet users, analyze various indexes of the complaints, improve the satisfaction degree and reduce the cost.
Description
Technical Field
The invention relates to the field of complaint prediction systems, in particular to a complaint prediction method for a mobile phone internet user.
Background
For telecommunication operators and mobile internet, the method takes users as the center, pays attention to user requirements and user experience, reduces the complaint rate of the users, and improves the satisfaction degree of the users, which is the key work content of daily operation.
The traditional user complaint processing method is to respond after a user initiatively initiates complaints, has the defects of long complaint response time, low complaint user processing satisfaction degree and the like, cannot analyze and reform main network blind spots, and is difficult to meet the current fierce market competition requirements and higher service timeliness requirements.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems in the background technology, the invention provides a method for predicting complaints of users accessing the internet by a mobile phone, which can accurately predict the complaints of the users of the mobile internet, and analyze various indexes of the complaints, thereby improving the satisfaction degree and reducing the cost.
(II) technical scheme
In order to solve the problems, the invention provides a method for predicting complaints of users who surf the internet by a mobile phone, which comprises a complaint data acquisition module, a data index acquisition module, an application category data acquisition module, a cost and flow index acquisition module, a surfing positioning acquisition module and a prediction analysis system;
the complaint data acquisition module, the data index acquisition module, the application type data acquisition module, the cost and flow index acquisition module and the internet surfing positioning acquisition module are all connected with the prediction analysis signal and data transmission;
the complaint data acquisition module is used for acquiring the number and time information of the mobile phone internet complaint;
the data index acquisition module is used for acquiring network data of the mobile phone on the Internet;
the application type data acquisition module is used for acquiring the internet application type of the mobile phone;
the system comprises a cost and flow index acquisition module, a flow rate acquisition module and a flow rate acquisition module, wherein the cost and flow rate index acquisition module is used for acquiring cost consumption and flow rate consumption indexes within at least half a year and reflecting the consumption personality of a mobile phone user;
the internet positioning acquisition module is used for acquiring the statistics of mass flow consumption places of mobile phone users;
and the prediction analysis system is used for calculating and processing the user complaint information, the mobile phone internet network data index, the application category data, the charging system data index and the internet positioning data to generate a complaint characteristic data set, and generating a prediction model by the obtained complaint characteristic data set through a machine learning algorithm.
Preferably, the predictive analysis system comprises a prediction module and an output analysis module;
the prediction module is used for inputting the data obtained by the acquisition module into a prediction model for prediction, predicting an original prediction data set by using the prediction obtaining model, screening out noise samples according to a prediction result, removing the noise samples in the positive samples and the negative samples, clustering the positive samples and the negative samples respectively, oversampling and undersampling the positive samples and the negative samples respectively according to the clustering result, and finally obtaining the prediction model;
and the output and analysis module is used for inputting the relevant data of the acquisition module into the prediction module, acquiring the complaint risk value of the mobile phone internet user to be predicted, analyzing and acquiring the relevance between the complaint risk and the main internet access place, and identifying the network quality of the internet access place.
According to the invention, the complaint of the mobile internet user can be accurately predicted, and various indicators of the complaint are analyzed, so that the satisfaction is improved, and the cost is reduced.
According to the method and the device, an accurate complaint prediction model of the mobile phone internet user can be trained and established, so that the complaint prediction of the mobile phone internet user is more accurate, the customer perception is improved, and the customer complaint processing efficiency is improved. Meanwhile, the intensity distribution of signals in the network can be analyzed and improved.
Drawings
Fig. 1 is a flowchart of a method for predicting complaints of a user accessing the internet by a mobile phone according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, the method for predicting complaints of a mobile phone internet user provided by the invention comprises a complaint data acquisition module, a data index acquisition module, an application category data acquisition module, a cost and flow index acquisition module, an internet positioning acquisition module and a prediction analysis system;
the complaint data acquisition module, the data index acquisition module, the application type data acquisition module, the cost and flow index acquisition module and the internet surfing positioning acquisition module are all connected with the prediction analysis signal and data transmission;
the complaint data acquisition module is used for acquiring the number and time information of the mobile phone internet complaint;
the data index acquisition module is used for acquiring network data of the mobile phone on the Internet;
the application type data acquisition module is used for acquiring the internet application type of the mobile phone;
the system comprises a cost and flow index acquisition module, a flow rate acquisition module and a flow rate acquisition module, wherein the cost and flow rate index acquisition module is used for acquiring cost consumption and flow rate consumption indexes within at least half a year and reflecting the consumption personality of a mobile phone user;
the internet positioning acquisition module is used for acquiring the statistics of mass flow consumption places of mobile phone users;
and the prediction analysis system is used for calculating and processing the user complaint information, the mobile phone internet network data index, the application category data, the charging system data index and the internet positioning data to generate a complaint characteristic data set, and generating a prediction model by the obtained complaint characteristic data set through a machine learning algorithm.
According to the method and the device, an accurate complaint prediction model of the mobile phone internet user can be trained and established, so that the complaint prediction of the mobile phone internet user is more accurate, the customer perception is improved, and the customer complaint processing efficiency is improved. Meanwhile, the intensity distribution of signals in the network can be analyzed and improved, so that the cost is reduced.
In an alternative embodiment, a predictive analysis system includes a prediction module and an output analysis module;
the prediction module is used for inputting the data obtained by the acquisition module into a prediction model for prediction, predicting an original prediction data set by using the prediction obtaining model, screening out noise samples according to a prediction result, removing the noise samples in the positive samples and the negative samples, clustering the positive samples and the negative samples respectively, oversampling and undersampling the positive samples and the negative samples respectively according to the clustering result, and finally obtaining the prediction model;
and the output and analysis module is used for inputting the relevant data of the acquisition module into the prediction module, acquiring the complaint risk value of the mobile phone internet user to be predicted, analyzing and acquiring the relevance between the complaint risk and the main internet access place, and identifying the network quality of the internet access place.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (2)
1. The method for predicting the complaint of the mobile phone internet user is characterized by comprising a complaint data acquisition module, a data index acquisition module, an application category data acquisition module, a cost and flow index acquisition module, an internet positioning acquisition module and a prediction analysis system;
the complaint data acquisition module, the data index acquisition module, the application type data acquisition module, the cost and flow index acquisition module and the internet surfing positioning acquisition module are all connected with the prediction analysis signal and data transmission;
the complaint data acquisition module is used for acquiring the number and time information of the mobile phone internet complaint;
the data index acquisition module is used for acquiring network data of the mobile phone on the Internet;
the application type data acquisition module is used for acquiring the internet application type of the mobile phone;
the system comprises a cost and flow index acquisition module, a flow rate acquisition module and a flow rate acquisition module, wherein the cost and flow rate index acquisition module is used for acquiring cost consumption and flow rate consumption indexes within at least half a year and reflecting the consumption personality of a mobile phone user;
the internet positioning acquisition module is used for acquiring the statistics of mass flow consumption places of mobile phone users;
and the prediction analysis system is used for calculating and processing the user complaint information, the mobile phone internet network data index, the application category data, the charging system data index and the internet positioning data to generate a complaint characteristic data set, and generating a prediction model by the obtained complaint characteristic data set through a machine learning algorithm.
2. The method for predicting complaints of users who surf the internet by using mobile phones according to claim 1, wherein the prediction analysis system comprises a prediction module and an output analysis module;
the prediction module is used for inputting the data obtained by the acquisition module into a prediction model for prediction, predicting an original prediction data set by using the prediction obtaining model, screening out noise samples according to a prediction result, removing the noise samples in the positive samples and the negative samples, clustering the positive samples and the negative samples respectively, oversampling and undersampling the positive samples and the negative samples respectively according to the clustering result, and finally obtaining the prediction model;
and the output and analysis module is used for inputting the relevant data of the acquisition module into the prediction module, acquiring the complaint risk value of the mobile phone internet user to be predicted, analyzing and acquiring the relevance between the complaint risk and the main internet access place, and identifying the network quality of the internet access place.
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| CN201911360606.5A CN111210057A (en) | 2019-12-25 | 2019-12-25 | Method for predicting complaints of mobile phone internet users |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113099475A (en) * | 2021-04-20 | 2021-07-09 | 中国移动通信集团陕西有限公司 | Network quality detection method and device, electronic equipment and readable storage medium |
| CN113094567A (en) * | 2021-03-31 | 2021-07-09 | 四川新网银行股份有限公司 | Malicious complaint identification method and system based on text clustering |
| CN115564332A (en) * | 2022-10-08 | 2023-01-03 | 深圳中科保泰科技有限公司 | Government affair risk analysis method and system based on big data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106921507A (en) * | 2015-12-25 | 2017-07-04 | 株式会社日立制作所 | The method and apparatus being predicted to customer complaint within a wireless communication network |
| CN109982367A (en) * | 2017-12-28 | 2019-07-05 | 中国移动通信集团四川有限公司 | Mobile terminal Internet access customer complaint prediction technique, device, equipment and storage medium |
| CN110602652A (en) * | 2019-10-15 | 2019-12-20 | 中移信息技术有限公司 | Complaint model training method, and user complaint prediction method, device and equipment |
-
2019
- 2019-12-25 CN CN201911360606.5A patent/CN111210057A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106921507A (en) * | 2015-12-25 | 2017-07-04 | 株式会社日立制作所 | The method and apparatus being predicted to customer complaint within a wireless communication network |
| CN109982367A (en) * | 2017-12-28 | 2019-07-05 | 中国移动通信集团四川有限公司 | Mobile terminal Internet access customer complaint prediction technique, device, equipment and storage medium |
| CN110602652A (en) * | 2019-10-15 | 2019-12-20 | 中移信息技术有限公司 | Complaint model training method, and user complaint prediction method, device and equipment |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113094567A (en) * | 2021-03-31 | 2021-07-09 | 四川新网银行股份有限公司 | Malicious complaint identification method and system based on text clustering |
| CN113099475A (en) * | 2021-04-20 | 2021-07-09 | 中国移动通信集团陕西有限公司 | Network quality detection method and device, electronic equipment and readable storage medium |
| CN115564332A (en) * | 2022-10-08 | 2023-01-03 | 深圳中科保泰科技有限公司 | Government affair risk analysis method and system based on big data |
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