CN113962480A - Method for establishing business satisfaction degree model, business analysis method and device - Google Patents
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Abstract
The application provides a method for establishing a service satisfaction model, which comprises the following steps: acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform; training a first preset model according to the user signaling data, the user service data and historical user scoring data in a preset time period; determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model through the preset types of user scoring data; and establishing a business satisfaction model according to the trained first preset model and the trained second preset model. By adopting the method of the technical scheme, the satisfaction degree of the user to the service can be accurately estimated, so that an operator can adjust the service according to the predicted service satisfaction degree, and the service quality of the operator is effectively improved.
Description
Technical Field
The present application relates to the field of model building technologies, and in particular, to a method for building a business satisfaction model, a business analysis method, and a device.
Background
With the rapid development of mobile communication and internet technologies, the requirements of users on telecommunication networks and service quality are higher and higher. The operator needs to pay attention not only to the coverage of the network, but also to the user experience of the user from opening the service to logging off the number. Therefore, the user satisfaction becomes an important assessment index of the operator and an index for measuring the network quality of the operator.
However, at present, the analysis of the user satisfaction is not accurate, and the consideration of the personal factors of the user is lacking, so that a more accurate model is needed, the user satisfaction on the service can be accurately estimated, and further, the operator can adjust the service according to the predicted service satisfaction, so as to effectively improve the service quality of the operator.
Disclosure of Invention
The application provides a method for establishing a business satisfaction model, a business analysis method and a business analysis device, which are used for solving the problem that the analysis of the user satisfaction is inaccurate at present.
In a first aspect, the present application provides a method for establishing a service satisfaction model, where the method includes:
acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform;
training a first preset model according to the user signaling data, the user service data and historical user scoring data in a preset time period; the first preset model is used for outputting at least two types of user scoring data;
determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model according to the preset types of user scoring data; the second preset model is used for outputting user scoring data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions;
and establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model for determining the satisfaction degree of the user to the business.
Optionally, training a first preset model according to the user signaling data, the user service data, and historical user scoring data in a preset time period includes:
establishing a relationship between the user signaling data and the user service data according to the terminal number to obtain associated data;
and training the first preset model through the associated data and historical user scoring data in a preset time period.
Optionally, training the first preset model according to the associated data and historical user scoring data in a preset time period includes:
deleting and/or modifying data which do not meet preset conditions in the associated data to obtain modified associated data;
taking historical user scoring data in a preset time period as tag data;
and establishing a training data set and a testing data set by using the modified associated data and the data set of the label data, and training the first preset model.
Optionally, the method further includes:
and inputting the user signaling data to be predicted and the user service data to be predicted into the service satisfaction degree model, and determining the user scoring data of the user to be predicted.
In a second aspect, the present application provides a service analysis method, including:
user data to be analyzed is obtained, the user data to be analyzed is analyzed by adopting a service satisfaction model established by the method in any one of the first aspect, and service satisfaction information of a user in the user data to be analyzed is determined.
In a third aspect, the present application provides an apparatus for establishing a service satisfaction model, where the apparatus includes:
the acquisition module is used for acquiring user signaling data of at least one terminal number from the first data platform and acquiring user service data of at least one terminal number from the second data platform;
the first preset model training module is used for training a first preset model according to the user signaling data, the user service data and historical user grading data in a preset time period; the first preset model is used for outputting at least two types of user scoring data;
the second preset model training module is used for determining preset type user grading data from at least two types of user grading data output from the first preset model and training the second preset model according to the preset type user grading data; the second preset model is used for outputting user scoring data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions;
the establishing module is used for establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model used for determining the satisfaction degree of the user to the business.
Optionally, the first preset model training module includes:
the associated data determining unit is used for establishing a relationship between the user signaling data and the user service data according to the terminal number to obtain associated data;
and the first preset model training unit is used for training the first preset model through the associated data and historical user scoring data in a preset time period.
In a fourth aspect, the present application provides a traffic analyzing apparatus, including:
the acquisition module is used for acquiring user data to be analyzed;
a determining module, configured to analyze the user data to be analyzed by using the established service satisfaction model in the third aspect, and determine service satisfaction information of the user in the user data to be analyzed.
In a fifth aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method as described in the first or second aspect.
In a sixth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method as described in the first or second aspect when executed by a processor.
In a seventh aspect, the present application provides a computer program product comprising a computer program that, when executed by a processor, implements the method according to the first or second aspect.
The application provides a method for establishing a service satisfaction model, which comprises the following steps: acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform; training a first preset model according to the user signaling data, the user service data and historical user scoring data in a preset time period; the first preset model is used for outputting at least two types of user scoring data; determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model according to the preset types of user scoring data; the second preset model is used for outputting user scoring data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions; and establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model for determining the satisfaction degree of the user to the business. By adopting the method of the technical scheme, the satisfaction degree of the user to the service can be accurately estimated, the average absolute error value of the service satisfaction degree model is reduced, and further an operator can adjust the service according to the predicted service satisfaction degree, so that the service quality of the operator is effectively improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flowchart illustrating a method for establishing a service satisfaction model according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for establishing a service satisfaction model according to a second embodiment of the present application;
fig. 3a is a schematic diagram of a user age and a user score according to a second embodiment of the present application;
fig. 3b is a schematic diagram of user intra-provincial traffic and user score according to the second embodiment of the present application;
fig. 4 is a schematic flow chart of a service analysis method according to a third embodiment of the present application;
fig. 5 is a schematic diagram of an apparatus for establishing a service satisfaction model according to a fourth embodiment of the present application;
fig. 6 is a schematic diagram of an apparatus for establishing a service satisfaction model according to a fifth embodiment of the present application;
fig. 7 is a schematic diagram of a service analysis apparatus according to a sixth embodiment of the present application;
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a method for establishing a service satisfaction model according to an embodiment of the present application. The first embodiment comprises the following steps:
s101, obtaining user signaling data of at least one terminal number from a first data platform and obtaining user service data of at least one terminal number from a second data platform.
Illustratively, the first data platform is different from the second data platform, the first data platform may be a platform for storing user communication conditions, and the second data platform may be a market research platform. In this embodiment, the user signaling data of different terminal numbers are different, and the same terminal number has both user signaling data and user service data. Wherein, the user signaling data comprises: the home province of the terminal number, the province visited by the terminal number, the highest network access type, whether Voice over Long-Term Evolution (VOLTE) is used, the online duration of 2G, 3G, 4G, and 5G, the number of 2G calls, the number of 3G calls, the uplink traffic, the downlink traffic, Round-Trip Time (Round Trip Time, RTT) uplink delay, RTT uplink number, RTT downlink delay, RTT downlink number, Transmission Control Protocol (TCP) setup number, TCP delay, Hyper Text Transfer Protocol (HTTP) downlink rate, POST upload delay, up and down average packet interval jitter, total card pause duration, number of occurrence of service card pause, average value of signaling data of hour granularity, and the like. The user service data comprises: the service management method comprises the following steps of customer gender, customer age, user star level, whether broadband convergence service is available or not, package name, current package order time, package cost, date flow of use in province of nearly six months (average per-household per-month internet traffic, DOU for short), out-of-province DOU of nearly six months, out-of-package traffic of nearly six months, minutes of use in province of nearly six months (average per-user per-month call duration, MOU for short), out-of-province MOU of nearly six months, whether package is changed or not, package name before package is changed recently, hot line self-service times of nearly six months, hot line manual service times of nearly six months, total hot line dialing times of nearly six months, business office business handling times of nearly six months, network hall login times of nearly six months, complaint times of nearly six months, total call duration, total traffic, free traffic, charge traffic, terminal price, whether brand, contract, brand, whether or not, Whether the number is beautiful, etc.
S102, training a first preset model according to user signaling data, user service data and historical user scoring data in a preset time period; the first preset model is used for outputting at least two types of user scoring data.
Illustratively, the preset time period may be set by the user, and may be one month or two months. The historical user scoring data refers to the scoring condition of the user on the service in the historical record. For example, for a 4G service, users of different ages and different professions score the service. In this embodiment, the first preset model may be trained by the acquired user signaling data, user service data, and historical user scoring data within a preset time period. User signaling data, user service data and historical user scoring data in a preset time period are input into a first preset model, and different types of user scoring data can be output from the first preset model. The different types are that user scoring data are classified according to preset types and then output according to the preset types. For example, the preset types may be classified into 3 types, the first type is that the user rating data is 1, the second type is that the user rating data is 2-9, and the third type is that the user rating data is 10. Further, the first type of user scoring data, the second type of user scoring data and the third type of user scoring data can be output through the first preset model. In this embodiment, optionally, the first preset model may be a classification model. Specifically, the three classification models may be trained by eXtreme Gradient Boosting (Xgboost for short).
S103, determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model according to the preset types of user scoring data; the second preset model is used for outputting user grading data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type includes user rating data of at least two dimensions.
Illustratively, the method includes the steps of determining preset type user scoring data from content output from a first preset model, inputting the preset type user scoring data into a second preset model, subdividing the preset type user scoring data through the second preset model, and dividing the preset type user scoring data into multiple dimensions of user scoring data. For example, if the preset type of user rating data input into the second preset model is user rating data of 2-9 points, the user rating data of at least two dimensions may be output by presetting the second model, where the user rating data of at least two dimensions is user rating data of 1 point, the user rating data of 2 points, the user rating data of 3 points, the user rating data of 4 points, the user rating data of 5 points, the user rating data of 6 points, the user rating data of 7 points, the user rating data of 8 points, and the user rating data of 9 points. In this embodiment, optionally, the second preset model may be a regression model, and specifically, may be a random forest training regression model.
And S104, establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is used for determining the satisfaction degree of the user to the business.
Illustratively, the trained first preset model and the trained second preset model are combined, wherein an output value of the trained first preset model is an input value of the trained second preset model, and an output value of the trained second preset model is an output value of the business satisfaction model.
The application provides a method for establishing a service satisfaction model, which comprises the following steps: acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform; training a first preset model according to the user signaling data, the user service data and historical user scoring data in a preset time period; the first preset model is used for outputting at least two types of user grading data; determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model through the preset types of user scoring data; the second preset model is used for outputting user grading data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions; and establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model for determining the satisfaction degree of the user to the business. By adopting the method of the technical scheme, the satisfaction condition of the user can be reflected more comprehensively, the satisfaction of the user to the service is accurately estimated, and further the operator can adjust the service according to the predicted service satisfaction, so that the service quality of the operator is effectively improved.
Fig. 2 is a schematic flow chart of a method for establishing a service satisfaction model according to a second embodiment of the present application. The second embodiment comprises the following steps:
s201, obtaining user signaling data of at least one terminal number from a first data platform and obtaining user service data of at least one terminal number from a second data platform.
For example, this step may refer to step S101 described above, and is not described again.
S202, establishing a relation between the user signaling data and the user service data according to the terminal number to obtain associated data.
In this embodiment, the terminal number is a mobile phone number, and the terminal number may be used as a unique identifier of the user signaling data and the user service data. For example, the user signaling data with the terminal number of 130 x y x y x y x y x y x y x.
S203, training a first preset model through the associated data and historical user grading data in a preset time period.
Illustratively, after the associated data and the historical user scoring data within a preset time period are input into the first preset model, the first preset model is trained into a model capable of outputting user scoring data according to the associated data, and it is noted that the user scoring data is divided according to preset types. Further, the historical user score data in the preset time period may refer to the user age and user score diagram shown in fig. 3a and the user provincial traffic and user score diagram shown in fig. 3 b.
In this embodiment, optionally, training the first preset model by using the association data and the historical user score data in the preset time period includes: deleting and/or modifying data which do not meet preset conditions in the associated data to obtain modified associated data; taking historical user scoring data in a preset time period as tag data; and establishing a training data set and a testing data set by using the modified associated data and the data set of the label data, and training a first preset model. The ratio of the training data set to the test data set may be 7 to 3, and it should be noted that the ratio is only an example and is not limited.
In this embodiment, the preset condition may be whether a specific numerical value is a null value or a specific numerical value, or whether a numerical value requirement in a preset data item is within a preset threshold range. The modification of the data may include one or more of the following: missing value filling, abnormal value processing, normalization, single-hot coding, characteristic fusion and disassembly and the like.
S204, determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model according to the preset types of user scoring data; the second preset model is used for outputting user grading data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type includes user rating data of at least two dimensions.
For example, this step may refer to step S103 described above, and is not described again.
S205, establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is used for determining the satisfaction degree of the user to the business.
In this embodiment, the step may refer to the step S104, which is not described again.
S206, inputting the user signaling data to be predicted and the user service data to be predicted into the service satisfaction degree model, and determining the user grading data of the user to be predicted.
Illustratively, after acquiring the user signaling data to be predicted and the user service data to be predicted, inputting the data into the service satisfaction model, and determining the final user scoring data.
The application provides a method for establishing a service satisfaction model, which comprises the following steps: acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform; establishing a relationship between user signaling data and user service data according to the terminal number to obtain associated data; training a first preset model through the associated data and historical user scoring data in a preset time period; determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model through the preset types of user scoring data; establishing a business satisfaction model according to the trained first preset model and the trained second preset model; and inputting the user signaling data to be predicted and the user service data to be predicted into a service satisfaction degree model, and determining user grading data of the user to be predicted. By adopting the method of the technical scheme, the first preset model can be trained through historical user scoring data and associated data, the first preset model can be accurately obtained, the complexity of the business satisfaction model is reduced, the precision of the business satisfaction model is improved, and further the final business satisfaction model can predict the user scoring data of the user.
Fig. 4 is a schematic flow chart of a service analysis method according to a third embodiment of the present application. The third embodiment comprises the following steps:
s401, user data to be analyzed are obtained.
S402, analyzing the user data to be analyzed by adopting the service satisfaction model in the embodiment, and determining the service satisfaction information of the user in the user data to be analyzed.
The application provides a service analysis method, which comprises the following steps: acquiring user data to be analyzed; and analyzing the user data to be analyzed by adopting the service satisfaction model in the embodiment, and determining the service satisfaction information of the user in the user data to be analyzed. By adopting the method of the technical scheme, the user data to be analyzed can predict the service satisfaction conditions of some users through the service satisfaction model, so that the service can be adjusted in time, and the service can be more suitable for the users.
Fig. 5 is a schematic diagram of an apparatus for establishing a service satisfaction model according to a fourth embodiment of the present application. The apparatus 50 in the fourth embodiment includes the following:
an obtaining module 501, configured to obtain user signaling data of at least one terminal number from a first data platform and obtain user service data of at least one terminal number from a second data platform.
A first preset model training module 502, configured to train a first preset model according to user signaling data, user service data, and historical user rating data in a preset time period; the first preset model is used for outputting at least two types of user scoring data.
A second preset model training module 503, configured to determine preset types of user scoring data from the at least two types of user scoring data output from the first preset model, and train the second preset model according to the preset types of user scoring data; the second preset model is used for outputting user grading data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type includes user rating data of at least two dimensions.
The establishing module 504 is configured to establish a service satisfaction model according to the trained first preset model and the trained second preset model, where the service satisfaction model is a model used for determining the satisfaction degree of the user with respect to the service.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Fig. 6 is a schematic diagram of an apparatus for establishing a service satisfaction model according to a fifth embodiment of the present application. The apparatus 60 according to the fifth embodiment includes the following:
the obtaining module 601 is configured to obtain user signaling data of at least one terminal number from the first data platform and obtain user service data of at least one terminal number from the second data platform.
A first preset model training module 602, configured to train a first preset model according to user signaling data, user service data, and historical user rating data in a preset time period; the first preset model is used for outputting at least two types of user scoring data.
A second preset model training module 603, configured to determine preset types of user rating data from the at least two types of user rating data output from the first preset model, and train the second preset model according to the preset types of user rating data; the second preset model is used for outputting user grading data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type includes user rating data of at least two dimensions.
The establishing module 604 is configured to establish a service satisfaction model according to the trained first preset model and the trained second preset model, where the service satisfaction model is a model used for determining the satisfaction degree of the user with respect to the service.
In this embodiment, optionally, the first preset model training module 602 includes:
an associated data determining unit 6021, configured to establish a relationship between the user signaling data and the user service data according to the terminal number, so as to obtain associated data;
the first preset model training unit 6022 is configured to train a first preset model by associating the data with the historical user scoring data in a preset time period.
In this embodiment, optionally, the first preset model training unit 6022 includes:
an adjusting subunit 60221, configured to delete and/or modify data that does not meet a preset condition in the associated data to obtain modified associated data;
a tag data determination subunit 60222 configured to use historical user score data in a preset time period as tag data;
a training subunit 60223, configured to establish a training data set and a testing data set from the modified associated data and the data set of the label data, and train the first preset model.
In this embodiment, optionally, the apparatus further includes:
the determining module 605 is configured to input the user signaling data to be predicted and the user service data to be predicted into the service satisfaction model, and determine user rating data of the user to be predicted.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Fig. 7 is a schematic diagram of a service analysis apparatus according to a sixth embodiment of the present application, where the apparatus 70 in the sixth embodiment includes the following contents:
an obtaining module 701, configured to obtain user data to be analyzed.
The determining module 702 is configured to analyze the user data to be analyzed by using the service satisfaction model in the foregoing implementation, and determine service satisfaction information of the user in the user data to be analyzed.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 8 is a block diagram illustrating an electronic device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like, in accordance with an exemplary embodiment.
The apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method for establishing a service satisfaction model of the electronic device.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for establishing a business satisfaction model, the method comprising:
acquiring user signaling data of at least one terminal number from a first data platform and user service data of at least one terminal number from a second data platform;
training a first preset model according to the user signaling data, the user service data and historical user scoring data in a preset time period; the first preset model is used for outputting at least two types of user scoring data;
determining preset types of user scoring data from at least two types of user scoring data output from the first preset model, and training a second preset model according to the preset types of user scoring data; the second preset model is used for outputting user scoring data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions;
and establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model for determining the satisfaction degree of the user to the business.
2. The method of claim 1, wherein training a first predetermined model based on the user signaling data, the user traffic data, and historical user scoring data over a predetermined time period comprises:
establishing a relationship between the user signaling data and the user service data according to the terminal number to obtain associated data;
and training the first preset model through the associated data and historical user scoring data in a preset time period.
3. The method of claim 2, wherein training the first predetermined model with the correlation data and historical user score data over a predetermined time period comprises:
deleting and/or modifying data which do not meet preset conditions in the associated data to obtain modified associated data;
taking historical user scoring data in a preset time period as tag data;
and establishing a training data set and a testing data set by using the modified associated data and the data set of the label data, and training the first preset model.
4. A method according to any of claims 1-3, characterized in that the method further comprises:
and inputting the user signaling data to be predicted and the user service data to be predicted into the service satisfaction degree model, and determining the user scoring data of the user to be predicted.
5. A method for traffic analysis, the method comprising:
acquiring user data to be analyzed, analyzing the user data to be analyzed by adopting a service satisfaction model established by the method of any one of claims 1-4, and determining service satisfaction information of a user in the user data to be analyzed.
6. An apparatus for establishing a business satisfaction model, the apparatus comprising:
the acquisition module is used for acquiring user signaling data of at least one terminal number from the first data platform and acquiring user service data of at least one terminal number from the second data platform;
the first preset model training module is used for training a first preset model according to the user signaling data, the user service data and historical user grading data in a preset time period; the first preset model is used for outputting at least two types of user scoring data;
the second preset model training module is used for determining preset type user grading data from at least two types of user grading data output from the first preset model and training the second preset model according to the preset type user grading data; the second preset model is used for outputting user scoring data of at least two dimensions; wherein each type of user rating data comprises user rating data for at least one dimension; the preset type comprises user scoring data of at least two dimensions;
the establishing module is used for establishing a business satisfaction model according to the trained first preset model and the trained second preset model, wherein the business satisfaction model is a model used for determining the satisfaction degree of the user to the business.
7. A traffic analyzing apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring user data to be analyzed;
a determining module, configured to analyze the user data to be analyzed by using the service satisfaction model established in claim 6, and determine service satisfaction information of the user in the user data to be analyzed.
8. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-4 or claim 5.
9. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-4 or claim 5.
10. A computer program product, comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-4 or claim 5.
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