CN114493636A

CN114493636A - User satisfaction determining method and device, electronic equipment and storage medium

Info

Publication number: CN114493636A
Application number: CN202210094627.2A
Authority: CN
Inventors: 赵占伟; 傅强; 徐涛; 梁彧; 阿曼太; 蔡琳; 杨满智; 田野; 王杰; 金红; 陈晓光
Original assignee: Eversec Beijing Technology Co Ltd
Current assignee: Eversec Beijing Technology Co Ltd
Priority date: 2022-01-26
Filing date: 2022-01-26
Publication date: 2022-05-13

Abstract

The embodiment of the invention discloses a user satisfaction degree determining method and device, electronic equipment and a storage medium. The user satisfaction determining method comprises the following steps: acquiring user log data of a user; determining a user satisfaction index incidence matrix of a user satisfaction index according to the user log data; determining at least one user satisfaction key index according to the user satisfaction index incidence matrix; determining a user satisfaction degree key index incidence matrix according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index; determining a key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index incidence matrix; and determining a user satisfaction score according to each key point index satisfaction score so as to determine the user satisfaction. The technical scheme of the embodiment of the invention can quickly and accurately determine the user satisfaction, thereby improving the efficiency and the accuracy of determining the user satisfaction.

Description

User satisfaction determining method and device, electronic equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of computers, in particular to a user satisfaction degree determining method and device, electronic equipment and a storage medium.

Background

In recent years, customer satisfaction survey has been generally regarded at home and abroad, and particularly, customer satisfaction survey in the service industry has become one of important means for enterprises to find problems and improve services. It is important to improve the service work in a targeted manner by investigating and knowing the requirements of customers, the problems of enterprises and the differences between the enterprises and competitors through satisfaction.

The existing methods for determining user satisfaction generally include the following two methods: one is to establish a platform for user experience management, detect various quantitative indexes of a user in real time, and optimize and enhance user experience through complicated rules and manual modes. And the other method is to judge whether the user is an unsatisfactory user or not through keyword matching based on detection and identification of feedback contents of user experience research.

However, the first method relies on the existing index rule data accumulation through a complicated rule and manual judgment scheme, has poor timeliness and slow updating, and cannot locate an unsatisfactory user in time. The second method determines whether the user is an unsatisfactory user by keyword matching, and the user satisfaction cannot be accurately determined.

Disclosure of Invention

The embodiment of the invention provides a user satisfaction determining method and device, electronic equipment and a storage medium, which can quickly and accurately determine the user satisfaction, thereby improving the efficiency and accuracy of determining the user satisfaction.

According to an aspect of the present invention, there is provided a user satisfaction determining method including:

acquiring user log data of a user;

determining a user satisfaction index incidence matrix of a user satisfaction index according to the user log data;

determining at least one user satisfaction key index according to the user satisfaction index incidence matrix;

determining a user satisfaction degree key index incidence matrix according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index;

determining a key index satisfaction score of each user satisfaction key index according to the user satisfaction key index incidence matrix;

and determining a user satisfaction score according to each key point index satisfaction score so as to determine the user satisfaction.

According to another aspect of the present invention, there is provided a user satisfaction determining apparatus including:

the user log data acquisition module is used for acquiring user log data of a user;

the user satisfaction index incidence matrix determining module is used for determining a user satisfaction index incidence matrix of the user satisfaction index according to the user log data;

the user satisfaction degree key index determining module is used for determining at least one user satisfaction degree key index according to the user satisfaction degree index incidence matrix;

the user satisfaction degree key index incidence matrix determining module is used for determining a user satisfaction degree key index incidence matrix according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index;

the key index satisfaction degree score determining module is used for determining the key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index incidence matrix;

and the user satisfaction determining module is used for determining the user satisfaction score according to each key index satisfaction score so as to determine the user satisfaction.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a user satisfaction determination method according to any of the embodiments of the invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the user satisfaction determination method according to any of the embodiments of the present invention when executed.

The technical proposal of the embodiment of the invention obtains the user log data of the user and determines the user satisfaction index incidence matrix of the user satisfaction index according to the user log data, determining at least one user satisfaction key index according to the user satisfaction index correlation matrix, determining a user satisfaction key index correlation matrix according to the user satisfaction index correlation matrix and each user satisfaction key index, the key index satisfaction degree score of each user satisfaction degree key index is determined according to the user satisfaction degree key index incidence matrix, so that the user satisfaction degree score is determined according to each key index satisfaction degree score, the user satisfaction is further determined, the problems that the user satisfaction cannot be determined in time and the user satisfaction cannot be determined accurately by the existing user satisfaction determining method are solved, the user satisfaction can be determined quickly and accurately, and therefore the efficiency and the accuracy of determining the user satisfaction are improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a user satisfaction determining method according to an embodiment of the present invention;

fig. 2 is a flowchart of a user satisfaction determining method according to a second embodiment of the present invention;

fig. 3 is an exemplary flowchart of a user satisfaction determining method according to a second embodiment of the present invention;

fig. 4 is a schematic diagram of a user satisfaction determining apparatus according to a third embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device implementing the user satisfaction determination method according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be 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.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a user satisfaction determining method according to an embodiment of the present invention, where the present embodiment is applicable to a situation of determining user satisfaction quickly and accurately, and the method may be executed by a user satisfaction determining apparatus, where the apparatus may be implemented by software and/or hardware, and may generally be directly integrated in an electronic device that executes the method, where the electronic device may be a terminal device or a server device. Specifically, as shown in fig. 1, the method for determining user satisfaction may specifically include the following steps:

and S110, acquiring user log data of the user.

The user log data may be data for recording user behavior, for example, data for recording user internet behavior, data for recording user transaction behavior, and the like, which is not limited in the embodiment of the present invention.

In the embodiment of the invention, the user log data of the user is obtained, so that the user satisfaction index incidence matrix of the user satisfaction index is determined according to the user log data. It will be appreciated that user satisfaction may be determined by analyzing user log data to determine user behavior.

And S120, determining a user satisfaction index incidence matrix of the user satisfaction index according to the user log data.

The user satisfaction index may be an index capable of representing the user satisfaction, such as an internet access delay rate or a fallback rate, which is not limited in the embodiment of the present invention. The user satisfaction index correlation matrix may be a matrix constructed from user satisfaction indices. It will be appreciated that the user satisfaction index correlation matrix may include at least two matrix elements of a user and a user satisfaction index.

In the embodiment of the invention, after the user log data of the user is acquired, the user satisfaction index incidence matrix of the user satisfaction index can be further determined according to the user log data. For example, if the user satisfaction index is the internet access delay rate, the user satisfaction index association matrix of the user satisfaction index may be determined according to the internet access request time and the internet access connection time in the user log data.

S130, determining at least one user satisfaction key index according to the user satisfaction index incidence matrix.

The user satisfaction key index may be an index obtained by screening a user satisfaction index. It can be understood that there may be a plurality of user satisfaction indicators, but when the number of the user satisfaction indicators is more and more, the weight that each user satisfaction indicator affects the user satisfaction is more fuzzy, and therefore, the number of the user satisfaction indicators needs to be simplified to obtain the user satisfaction key indicator, that is, the user satisfaction key indicator may be an indicator in the user satisfaction indicator. For example, if the user satisfaction index includes an index a, an index B, an index C, an index D, and an index E, the user satisfaction key index may be the index a and the index B, or the index B, the index C, the index D, and the like, which is not limited in the embodiment of the present invention.

In the embodiment of the invention, after the user satisfaction index incidence matrix of the user satisfaction index is determined according to the user log data, at least one user satisfaction key index can be further determined according to the user satisfaction index incidence matrix.

And S140, determining a user satisfaction key index incidence matrix according to the user satisfaction index incidence matrix and each user satisfaction key index.

The user satisfaction degree key index incidence matrix can be a matrix constructed by the user satisfaction degree key indexes. It is to be understood that the user satisfaction highlight index correlation matrix may include at least two matrix elements of the user and the user satisfaction highlight index.

In the embodiment of the invention, after at least one user satisfaction degree key index is determined according to the user satisfaction degree index key matrix, the user satisfaction degree key index key matrix can be further determined according to the user satisfaction degree index key matrix and each user satisfaction degree key index. Illustratively, the user satisfaction degree key index incidence matrix is determined according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index, and the user satisfaction degree key index incidence matrix can be constructed by extracting each user satisfaction degree key index in the user satisfaction degree index incidence matrix.

S150, determining the key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index incidence matrix.

The key index satisfaction score may be a user satisfaction score for a user satisfaction key index. For example, if the key user satisfaction index is the internet access delay rate, the key user satisfaction score may be a user satisfaction score for the internet access delay rate.

In the embodiment of the invention, after the user satisfaction degree key index incidence matrix is determined according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index, the key index satisfaction degree score of each user satisfaction degree key index can be further determined according to the user satisfaction degree key index incidence matrix.

And S160, determining a user satisfaction score according to each key index satisfaction score so as to determine the user satisfaction.

The user satisfaction score may be a user overall satisfaction score, for example, a user internet experience satisfaction score, a user shopping experience satisfaction score, and the like, which is not limited in the embodiments of the present invention.

In the embodiment of the invention, after the key index satisfaction degree score of each user satisfaction degree key index is determined according to the user satisfaction degree key index incidence matrix, the user satisfaction degree score can be further determined according to each key index satisfaction degree score so as to determine the user satisfaction degree. For example, the user satisfaction score is determined according to each key point index satisfaction score, the user satisfaction score may be determined according to a sum of the key point index satisfaction scores, or the user satisfaction score may be determined according to a weighted value of the key point index satisfaction scores, which is not limited in the embodiments of the present invention.

The technical scheme of the embodiment obtains the user log data of the user, determines the user satisfaction index incidence matrix of the user satisfaction index according to the user log data, determining at least one user satisfaction key index according to the user satisfaction index correlation matrix, determining a user satisfaction key index correlation matrix according to the user satisfaction index correlation matrix and each user satisfaction key index, the key index satisfaction degree score of each user satisfaction degree key index is determined according to the user satisfaction degree key index incidence matrix, so that the user satisfaction degree score is determined according to each key index satisfaction degree score, the user satisfaction is further determined, the problems that the user satisfaction cannot be determined in time and the user satisfaction cannot be determined accurately by the existing user satisfaction determining method are solved, the user satisfaction can be determined quickly and accurately, and therefore the efficiency and the accuracy of determining the user satisfaction are improved.

Example two

Fig. 2 is a flowchart of a user satisfaction determining method according to a second embodiment of the present invention, which further refines the above technical solutions, and provides a user satisfaction index incidence matrix that determines user satisfaction indexes according to user log data, determines at least one user satisfaction key index according to the user satisfaction index incidence matrix, determines a key index satisfaction score of each user satisfaction key index according to the user satisfaction key index incidence matrix, and determines multiple specific selectable implementation manners of the user satisfaction score according to each key index satisfaction score. The solution in this embodiment may be combined with the individual alternatives in one or more of the embodiments described above. As shown in fig. 2, the method may include the steps of:

s210, obtaining user log data of the user.

Optionally, the user log data may include user internet log data; the user internet log data may include user internet service log data and user internet signaling log data.

The user internet log data may be data for recording the user internet behavior. The log data of the user internet service may be internet log data for recording various services of the user, for example, the log data may be Voice over LTE (4G high definition Voice service) call data, video data, web page data, instant messaging data, music data, game data, or the like, which is not limited in the embodiment of the present invention. The user internet access signaling log data may be data recording a signaling log generated when the user accesses the internet. Optionally, the user internet access signaling log data may include user internet access network signaling log data and user internet access MR (Measurement Report) signaling log data. For example, the user internet service log data may include Mobile phone number data, start time data, end time data, IMEI (International Mobile Equipment Identity) data, IMSI (International Mobile Subscriber Identity) data, ECI (E-UTRAN Cell Identity) data, service duration data, service status data, or the like. The network signaling log data of the user access network can comprise: mobile phone number data, start time data, end time data, IMEI data, IMSI data, ECI data, flow type data or flow status data, etc. The user internet access MR signaling log data may include: start time data, end time data, ECI data, RSRP (Reference Signal Receiving Power) measurement value data, RSRQ (Reference Signal Receiving Quality) measurement value data, TADV (Timing advance) measurement value data, and the like.

Specifically, the obtaining of the user log data of the user may be obtaining of user internet service log data and user internet signaling log data of the user. Alternatively, the user log data may include user log data for the current day and N days before the history. The method comprises the steps of obtaining user internet service log data and user internet signaling log data of a user, and obtaining the log data according to a preset collection standard.

S220, determining user satisfaction index data of at least one user satisfaction index according to the user log data; the user satisfaction index data is index data of a combined dimension of a user mobile phone number dimension, a time dimension, a communication cell dimension and a communication base station dimension.

The user satisfaction index data may be index value data of a user satisfaction index.

In the embodiment of the present invention, after the user log data of the user is obtained, the user satisfaction index data of at least one user satisfaction index may be further determined according to the user log data. Specifically, the user satisfaction index data may be index data of a combination dimension of a user mobile phone number dimension, a time dimension, a communication cell dimension, and a communication base station dimension. For example, the user satisfaction index data may be index data of the user mobile phone number a, time a, the communication cell a, and the communication base station a, may also be index data of the user mobile phone number a, time B, the communication cell a, and the communication base station a, or may also be index data of the user mobile phone number a, time B, the communication cell a, and the communication base station B, and the like, which is not limited in this embodiment of the present invention.

Optionally, the user satisfaction index may include a success/failure rate type user satisfaction index, a delay type user satisfaction index, and a measurement type user satisfaction index. Specifically, user log data are aggregated according to the combined dimensions of the mobile phone number dimension, the time dimension, the communication cell dimension and the communication base station dimension of the user, success/failure times and request times can be determined through accumulation, and user satisfaction index data of success/failure rate type user satisfaction indexes are determined according to the percentages of the success/failure times and the request times; the total time delay and the total number of requests can be determined through accumulation, and user satisfaction index data of time delay type user satisfaction indexes are determined according to the total time delay divided by the total number of requests; the total measurement value and the total number of measurements may be determined by accumulation and the user satisfaction index data for the measurement-like user satisfaction index may be determined from the total measurement value divided by the total number of measurements.

S230, establishing a user satisfaction index incidence matrix according to the user satisfaction index data; wherein the matrix elements of the user satisfaction index incidence matrix include: the user mobile phone number dimension, the time dimension, the communication cell dimension, the communication base station dimension, and the user satisfaction index data.

In the embodiment of the invention, after the user satisfaction index data of at least one user satisfaction index is determined according to the user log data, a user satisfaction index incidence matrix can be further established according to the user satisfaction index data. Specifically, the matrix elements of the user satisfaction index incidence matrix include: user mobile phone number dimension, time dimension, communication cell dimension, communication base station dimension and user satisfaction index data. An exemplary user satisfaction index correlation matrix is shown in table 1.

TABLE 1 user satisfaction index correlation matrix

Optionally, if the user satisfaction index data of a certain dimension cannot be determined according to the user log data, the mode value of the user satisfaction index may be determined as the user satisfaction index data. Illustratively, as shown in Table 1, if data 1-2 cannot be determined from user log data, data 1-2 may be determined from the mode values of data 1-1, data 1-3, and data 1-4.

And S240, acquiring the target incidence matrix element data of the user satisfaction index incidence matrix.

The target incidence matrix element data can be target data in matrix elements of the user satisfaction index incidence matrix. For example, as shown in table 1, the target incidence matrix element data may be data 1-1, data 2-1, data 3-2, or the like, which is not limited by the embodiment of the present invention.

In the embodiment of the invention, after the user satisfaction index incidence matrix is established according to the user satisfaction index data, the target incidence matrix element data of the user satisfaction index incidence matrix can be further obtained so as to carry out normalization processing on the target incidence matrix element data.

And S250, carrying out normalization processing on the element data of the target incidence matrix to obtain a user satisfaction index normalized incidence matrix.

The user satisfaction index normalized incidence matrix can be a matrix obtained by performing normalization processing on element data of a target incidence matrix.

In the embodiment of the invention, after the target incidence matrix element data of the user satisfaction index incidence matrix is obtained, the target incidence matrix element data can be further subjected to normalization processing to obtain the user satisfaction index normalized incidence matrix. It is understood that the user satisfaction index may include an index having a higher satisfaction degree as the index value is larger and an index having a higher satisfaction degree as the index value is smaller.

Optionally, if the user satisfaction index is an index with a larger index value and a higher satisfaction, when normalization processing is performed on the target incidence matrix element data of the user satisfaction index, a formula (y-min)/(max-min) may be used to determine normalized data corresponding to the target incidence matrix element data in the user satisfaction index normalized incidence matrix. If the user satisfaction index is an index with smaller index value and higher satisfaction, the normalized data corresponding to the target incidence matrix element data in the user satisfaction index normalized incidence matrix can be determined by adopting a formula (max-y)/(max-min) when the normalization processing is carried out on the target incidence matrix element data of the user satisfaction index. Wherein y is the target incidence matrix element data of the user satisfaction index, min is the minimum user satisfaction index data in the user satisfaction index, and max is the maximum user satisfaction index data in the user satisfaction index.

S260, determining a user satisfaction index covariance matrix of the user satisfaction index normalized correlation matrix according to the user satisfaction index normalized correlation matrix.

And S270, determining a characteristic value of the user satisfaction index according to the user satisfaction index covariance matrix.

The user satisfaction index covariance matrix may be a covariance matrix of the user satisfaction index. The satisfaction index characteristic value may be a characteristic value of a user satisfaction index.

In the embodiment of the invention, after the normalization processing is carried out on the original data of the target incidence matrix to obtain the user satisfaction index normalization incidence matrix, the user satisfaction index covariance matrix of the user satisfaction index normalization incidence matrix can be further determined according to the user satisfaction index normalization incidence matrix, and the user satisfaction index characteristic value is determined according to the user satisfaction index covariance matrix.

S280, sorting the user satisfaction index according to the user satisfaction index characteristic value, and determining at least one user satisfaction key index according to a user satisfaction index sorting result.

The user satisfaction index ranking result may be a result obtained by ranking the user satisfaction indexes.

In the embodiment of the invention, after the characteristic value of the user satisfaction index is determined according to the covariance matrix of the user satisfaction index, the user satisfaction index can be further sorted according to the characteristic value of the user satisfaction index, so that at least one user satisfaction key index is determined according to the sorting result of the user satisfaction index.

Optionally, the user satisfaction index is sorted according to the user satisfaction index characteristic valueThe user satisfaction indexes can be sorted in a positive order according to the size of the characteristic value of the user satisfaction index. Illustratively, the user satisfaction index characteristic value of the user satisfaction index A is λ_AThe characteristic value of the user satisfaction index B is lambda_BThe characteristic value of the user satisfaction index C is lambda_CIf λ is_B>λ_A>λ_CIf the user satisfaction index ranking result is the user satisfaction index B->User satisfaction index A->User satisfaction index C.

Optionally, at least one user satisfaction key index is determined according to the user satisfaction index ranking result, and the user satisfaction key index may be determined according to the following formula:

wherein k represents the number of key indicators of user satisfaction, j represents the jth user satisfaction indicator, n represents the total number of user satisfaction indicators, and lambda_jA user satisfaction index characteristic value representing a jth user satisfaction index.

Specifically, the value of k can be determined according to the formula, that is, the number of the user satisfaction key indexes can be determined according to the formula, and further, the first k user satisfaction indexes in the user satisfaction index ranking result can be used as the user satisfaction key indexes.

And S290, determining a user satisfaction degree key index incidence matrix according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index.

S2100, obtaining user satisfaction key index data and user satisfaction key index time data of each user satisfaction key index in the user satisfaction key index incidence matrix.

The user satisfaction degree key index data may be index value data of a user satisfaction degree key index. The user satisfaction key index time data may be time data corresponding to the user satisfaction key index. For example, as shown in table 1, the user satisfaction emphasis index time data may be time 1, time 2, time 3, or the like, which is not limited in this embodiment of the present invention.

In the embodiment of the invention, after the user satisfaction degree key index incidence matrix is determined according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index, the user satisfaction degree key index data and the user satisfaction degree key index time data of each user satisfaction degree key index in the user satisfaction degree key index incidence matrix can be further obtained.

And S2110, sequencing each user satisfaction degree key index according to each user satisfaction degree key index data and each user satisfaction degree key index time data to obtain first limit index data and second limit index data of each user satisfaction degree key index.

The first limit index data may be user satisfaction highlight index data corresponding to one limit of the user satisfaction highlight index. The second limit index data may be user satisfaction highlight index data corresponding to another limit of the user satisfaction highlight index.

In the embodiment of the invention, after the user satisfaction degree key index data and the user satisfaction degree key index time data of each user satisfaction degree key index in the user satisfaction degree key index incidence matrix are obtained, the user satisfaction degree key indexes can be further sequenced according to the user satisfaction degree key index data and the user satisfaction degree key index time data, so that the first limit index data and the second limit index data of each user satisfaction degree key index are obtained.

Optionally, the first limit index data may be user satisfaction key index data corresponding to the user satisfaction key index at a 20% ranking position after ranking each user satisfaction key index. The second limit index data may be user satisfaction key index data corresponding to the user satisfaction key index at 80% of the ranking positions after ranking each user satisfaction key index. Illustratively, if there are 10 user satisfaction emphasis indexes, after the ranking process is performed on each user satisfaction emphasis index, the sequencing result of the user satisfaction key indexes is a user satisfaction key index 3- > a user satisfaction key index 5- > a user satisfaction key index 2- > a user satisfaction key index 6- > a user satisfaction key index 4- > a user satisfaction key index 1- > a user satisfaction key index 8- > a user satisfaction key index 10- > a user satisfaction key index 9- > a user satisfaction key index 7, the first boundary index data may be the user satisfaction highlight index data corresponding to the user satisfaction highlight index 5, and the second boundary index data may be the user satisfaction highlight index data corresponding to the user satisfaction highlight index 10.

Optionally, the user satisfaction key indexes are sorted according to the user satisfaction key index data and the user satisfaction key index time data, and the user satisfaction key index data of the same user satisfaction key index on the same day can be sorted in a positive order. Illustratively, if the user satisfaction key index data of the user satisfaction key index 1 of the user mobile phone number a is data 1, the user satisfaction key index time data is time 1, the user satisfaction key index data of the user satisfaction key index 1 of the user mobile phone number B is data 2, the user satisfaction key index time data is time 1, the user satisfaction key index data of the user satisfaction key index 1 of the user mobile phone number C is data 3, and the user satisfaction key index time data is time 2, the user satisfaction key indexes 1 of different user mobile phone numbers at time 1 are sorted, that is, the data 1 of the user mobile phone number a and the data 2 of the user mobile phone number B are sorted in the positive order according to the size of the data 1 and the data 2.

Optionally, after the user satisfaction major index data of the same user satisfaction major index on the same day are sorted in the positive order, daily first limit index data and daily second limit index data of the user satisfaction major index may be further determined, an average value of the daily first limit index data is used as the first limit index data of the user satisfaction major index, and an average value of the daily second limit index data is used as the second limit index data of the user satisfaction major index.

S2120, determining a key index satisfaction score of each user satisfaction key index according to the first limit index data and the second limit index data.

In the embodiment of the invention, after the user satisfaction degree key indexes are sequenced according to the user satisfaction degree key index data and the user satisfaction degree key index time data to obtain the first limit index data and the second limit index data of the user satisfaction degree key indexes, the key index satisfaction degree score of each user satisfaction degree key index can be further determined according to the first limit index data and the second limit index data.

Optionally, determining a key index satisfaction score of each user satisfaction key index according to the first limit index data and the second limit index data may include: determining the user satisfaction key index service attribute of each user satisfaction key index; determining the key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index service attribute and the following formula:

f(x)＝(1-p)g(x)+(10-g(x))p

wherein x represents user satisfaction key index data of each user satisfaction key index; under the condition that the user satisfaction degree key index service attribute is determined to be a first service attribute, p is a first calculation parameter, and a is a second limit index data parameter; and under the condition that the user satisfaction degree key index service attribute is determined to be a second service attribute, p is a second calculation parameter, and a is a first limit index data parameter.

The service attribute of the user satisfaction key index may be a service attribute of the user satisfaction key index, for example, the service attribute with a higher satisfaction degree may be referred to as a service attribute with a larger index value, or the service attribute with a higher satisfaction degree may be referred to as a service attribute with a smaller index value, which is not limited in the embodiment of the present invention. The first service attribute may be a user satisfaction emphasis index service attribute, for example, a service attribute with a smaller index value and a higher satisfaction. The second service attribute may be another user satisfaction emphasis index service attribute, for example, a service attribute with a higher satisfaction degree as the index value is larger. For example, the service attribute of the high-satisfaction-degree key index of the success-degree user may be a service attribute with a higher satisfaction degree if the index value is larger. The service attribute of the time delay type user satisfaction key index can be a service attribute with smaller index value and higher satisfaction.

Wherein the first calculation parameter may be a calculation parameter of a formula. The second calculation parameter may be another calculation parameter of the formula. For example, the value of the first calculation parameter may be 0, and the value of the second calculation parameter may be 1. The second margin indicator data parameter may be a parameter to which the second margin indicator data corresponds. The first limit indicator data parameter may be a parameter to which the first limit indicator data corresponds. For example, the second limit indicator parameter may take a value of 3.57 divided by the second limit indicator data, and the first limit indicator parameter may take a value of 2.97 divided by the first limit indicator data.

Specifically, after the user satisfaction degree key indexes are sorted according to the user satisfaction degree key index data and the user satisfaction degree key index time data to obtain the first limit index data and the second limit index data of the user satisfaction degree key indexes, the user satisfaction degree key index service attribute of each user satisfaction degree key index can be further determined, and the user satisfaction degree key index score of each user satisfaction degree key index is determined according to the user satisfaction degree key index service attribute and the formula. If the user satisfaction key index service attribute is the first service attribute, the parameter p of the above formula may be the first calculation parameter, and the parameter a may be the second limit index data parameter. If the user satisfaction key index service attribute is the second service attribute, the parameter p of the above formula may be the second calculation parameter, and the parameter a may be the first limit index data parameter. It is understood that the value range of the key point index satisfaction score can be 0-10 points.

And S2130, determining a user satisfaction score according to each key index satisfaction score so as to determine the user satisfaction.

Optionally, after determining at least one user satisfaction highlight index according to the user satisfaction index association matrix, the method may further include: and determining the weight value of the key index of each user satisfaction degree key index according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index. Determining the user satisfaction score according to each key index satisfaction score may include: and determining the user satisfaction score according to the key index satisfaction score and the key index weight value of each user satisfaction key index.

The key index weight value may be a weight of a key index of user satisfaction.

Specifically, after at least one user satisfaction degree key index is determined according to the user satisfaction degree index correlation matrix, a key index weight value of each user satisfaction degree key index can be further determined according to the user satisfaction degree index correlation matrix and each user satisfaction degree key index, and after the key index weight value of each user satisfaction degree key index is determined, a user satisfaction degree score is determined according to each key index satisfaction degree score and the key index weight value of each user satisfaction degree key index.

Optionally, the key index weight value of each user satisfaction degree key index is determined according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index, the target incidence matrix element data of the user satisfaction degree index incidence matrix can be obtained, normalization processing is carried out on the target incidence matrix element data, the user satisfaction degree index normalization incidence matrix is obtained, the user satisfaction degree index covariance matrix of the user satisfaction degree index normalization incidence matrix is determined according to the user satisfaction degree index normalization incidence matrix, the user satisfaction degree index characteristic value is determined according to the user satisfaction degree index covariance matrix, and each user satisfaction degree key index characteristic value is determined according to the user satisfaction degree index characteristic valueAnd the user satisfaction degree key index characteristic value of the index determines the key index weight value of each user satisfaction degree key index according to the user satisfaction degree key index characteristic value. Specifically, the weighted value of the key indicator of the user satisfaction degree is determined according to the characteristic value of the key indicator of the user satisfaction degree, which can be determined according to a formula

And formula

And determining the weight value of the key index of each user satisfaction degree key index. Wherein λ is_iAnd k represents the number of the user satisfaction key indexes.

Optionally, the user satisfaction score is determined according to each key index satisfaction score and the key index weight value of each user satisfaction key index, and the user satisfaction score can be determined by adding and summing the key index satisfaction score multiplied by the key index satisfaction score of each user satisfaction key index.

In a specific example, mobile terminal devices such as mobile phones are an indispensable important part of modern society, and besides normal communication functions, as people live on daily life continuously, more and more life services can be experienced on mobile terminals through networks, such as browsing websites, watching videos, shopping, listening to music or playing games. Of course, for different users, the experience of these network services is good or bad, and how to know the satisfaction degree of the user experience for the network service provider is more important in the current situation of "internet +" economy, because improving the user experience is a differentiated competitive advantage. Therefore, there is a need for a reasonable way to evaluate the network experience behavior of a user to help improve the satisfaction of the user experience.

Fig. 3 is an exemplary flowchart of a user satisfaction determining method according to a second embodiment of the present invention, and as shown in fig. 3, the method may specifically include the following:

(1) and acquiring the log of each service of the user on the internet (namely the log data of the service of the user on the internet) and the signaling log (namely the log data of the signaling of the user on the internet).

Specifically, the log and the signaling log data of the user mobile terminal on the internet within N days before the current day and the history are obtained. The specific internet access service may include VoLTE call, video, web page, instant messaging, music, game, etc., the internet access log and signaling content fields of various services are specified by a unified acquisition specification, and various service logs may at least include: the mobile phone number, the start time, the end time, the IMEI, the IMSI, the ECI, the service duration or the service state and other field contents. The network signaling log may include: mobile phone number, start time, end time, IMEI, IMSI, ECI, flow type or flow status, etc. The MR signaling log may include: start time, end time, ECI, RSRP measurement, RSRQ measurement, or TADV measurement, etc.

(2) And calculating the related indexes of the network experience.

And calculating index data of the combination dimensions of the user, the time, the ECI and the base station according to an index calculation method based on the user mobile terminal internet log and the signaling log in the current day and the historical N days.

The index calculation methods are roughly classified into the following categories:

a. success \ failure rate index: aggregating log data according to the combined dimensions, accumulating and solving success/failure times and request times, and finally solving the percentages of the success/failure times and the request times;

b. the time delay index is as follows: aggregating log data according to the combined dimensions, accumulating and solving 'total time delay' and 'total request times', and finally solving the value of 'total time delay' to 'total request times';

c. measurement type indexes: and aggregating the log data according to the combined dimensions, accumulating to obtain the total measurement value and the total measurement times, and finally obtaining the value of the total measurement value to the total measurement times.

(3) And generating a user index incidence matrix.

And extracting the affiliated index data according to the combined dimensionality of the user, the time, the ECI and the base station based on the number of the normal internet user in the mobile network. If the user has the index value corresponding to the index in the combined dimension, the position corresponding to the index is filled with the index value, otherwise, the mode value of the index is filled. And obtaining a relation matrix among the user, the time, the ECI, the base station and the index after the processing, wherein each row value of the matrix is used as a network experience track vector of the user.

(4) Screening key indexes, calculating the weight of the key indexes and defining a key index numerical baseline.

The mobile network user can generate corresponding internet logs in the process of using the network, and the influence of the network on the internet experience of the user can be objectively reflected under normal conditions by index data obtained by calculation through the internet logs of the user. When more services are used by the user through the network, namely the corresponding indexes are more, the quality of the user experience network can be more accurately reflected, and therefore the satisfaction degree of the user is influenced. However, when the number of indexes is more and more, the weight of each index influencing the satisfaction degree of the user is more fuzzy. Therefore, on the basis, the actual index number is simplified by using a key index screening method under the condition that the number of users is not changed by using the linear relation among the index values, so that the accuracy of weight judgment is realized.

a. Screening key indexes:

standard normalization processing is carried out on the index data; wherein, for the index of "the index data is better the bigger the formula is adopted: (x-min)/(max-min), the formula is adopted for the index of "the smaller the index data is, the better is: (max-x)/(max-min); calculating a covariance matrix, and calculating an eigenvalue lambda of the covariance matrix_jAnd according to the characteristic value lambda_jThe indexes are sorted according to the size of the indexes; determining important indicators according to the utilization rate of the information reaching 80% or more, i.e. according to a formula

And determining the k indexes as important indexes.

b. Calculating the weight of the key indexes:

calculating the weightCumulative information ratio of point index, i.e. cumulative variance contribution rate

And according to the formula

And calculating to obtain the weight of the key indexes.

c. Defining a key index value base line:

the method comprises the following specific steps of aiming at the screened key indexes to carry out index baseline definition: key index data of a user for multiple days are extracted from the user index incidence matrix; respectively carrying out positive sorting on all the index columns of each day to obtain index values of an upper bound value (a value at a position of 20%) and a lower bound value (a value at a position of 80%); and averaging the upper and lower boundary values calculated every day to obtain the final upper and lower boundary values of each index.

(5) And calculating the service index score and the satisfaction score of the user.

The distribution of a single index is subjected to gamma distribution, so that the satisfaction score is calculated in the following mode:

step 1: determining the service attribute of the screened index, namely, distinguishing the screened index into two types: the larger the index, the better the index, and the smaller the index, the higher the index, and the better the index, and the smaller the index, the better the index.

Step 2: calculating the single index score, wherein the algorithm formula of each index score of the user is as follows:

f(x)＝(1-p)g(x)+(10-g(x))p

wherein, x is the actual index value, and a and p are the parameters of the algorithm. When calculating the score for the "smaller is better" class index, p equals 0 and a equals the index lower bound value divided by 3.57. When calculating the score for the "larger is better" class index, p equals 1 and a equals the index upper bound divided by 2.97. The value range of f (x) is 0-10 minutes.

And step 3: and (4) calculating the user satisfaction score, multiplying the scores of the screening indexes by the index weight, and adding and summing to obtain the final satisfaction score.

(6) And outputting the predicted unsatisfied user.

And judging and outputting the judgment of the unsatisfied user depending on the satisfaction degree score obtained by the previous step, and judging the user as the unsatisfied user when the satisfaction degree score of the user is greater than or equal to 0 and less than or equal to 6.

According to the technical scheme, the satisfaction degree of the user using the network can be quickly and reasonably pre-judged according to the objective index reflecting the internet surfing condition of the user without human subjective intervention judgment, so that a network provider can conveniently find and position the end point condition of self service in time, the operation such as pacifying and the like can be accurately performed on the user unsatisfied in pre-judgment, and the user satisfaction degree is improved; the key index screening algorithm and the satisfaction degree score calculation algorithm are obtained through big data induction analysis verification, and a desired result can be obtained quickly and accurately.

According to the technical scheme of the embodiment, the user satisfaction index incidence matrix is established according to the user satisfaction index data by acquiring the user log data of the user and determining the user satisfaction index data of at least one user satisfaction index according to the user log data. And acquiring target incidence matrix element data of the user satisfaction index incidence matrix, performing normalization processing on the target incidence matrix element data to obtain a user satisfaction index normalized incidence matrix, determining a user satisfaction index covariance matrix of the user satisfaction index normalized incidence matrix according to the user satisfaction index normalized incidence matrix, determining a user satisfaction index characteristic value according to the user satisfaction index covariance matrix, performing sequencing processing on the user satisfaction index according to the user satisfaction index characteristic value to determine at least one user satisfaction key index according to a user satisfaction index sequencing result, and determining the user satisfaction key index incidence matrix according to the user satisfaction index incidence matrix and each user satisfaction key index. Obtaining user satisfaction degree key index data and user satisfaction degree key index time data of each user satisfaction degree key index in a user satisfaction degree key index incidence matrix, sequencing each user satisfaction degree key index according to each user satisfaction degree key index data and each user satisfaction degree key index time data to obtain first limit index data and second limit index data of each user satisfaction degree key index, determining key index satisfaction degree scores of each user satisfaction degree key index according to the first limit index data and the second limit index data, determining user satisfaction degree scores according to each key satisfaction degree score, determining user satisfaction degrees, solving the problems that the user satisfaction degrees cannot be determined in time and the user satisfaction degrees cannot be determined accurately by the existing user satisfaction degree determining method, and being capable of determining the user satisfaction degrees quickly and accurately, thereby improving the efficiency and accuracy of determining user satisfaction.

EXAMPLE III

Fig. 4 is a schematic diagram of a user satisfaction determining apparatus according to a third embodiment of the present invention, as shown in fig. 4, the apparatus includes: a user log data obtaining module 410, a user satisfaction index correlation matrix determining module 420, a user satisfaction key index determining module 430, a user satisfaction key index correlation matrix determining module 440, a key index satisfaction score determining module 450, and a user satisfaction determining module 460, wherein:

a user log data obtaining module 410, configured to obtain user log data of a user;

a user satisfaction index correlation matrix determining module 420, configured to determine a user satisfaction index correlation matrix of a user satisfaction index according to the user log data;

a user satisfaction degree key index determining module 430, configured to determine at least one user satisfaction degree key index according to the user satisfaction degree index association matrix;

a user satisfaction degree key index correlation matrix determining module 440, configured to determine a user satisfaction degree key index correlation matrix according to the user satisfaction degree index correlation matrix and each user satisfaction degree key index;

a key index satisfaction degree score determining module 450, configured to determine a key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index association matrix;

and the user satisfaction determining module 460 is configured to determine a user satisfaction score according to each of the key point index satisfaction scores to determine the user satisfaction.

Optionally, the user satisfaction index association matrix determining module 420 may be specifically configured to: determining user satisfaction index data of at least one user satisfaction index according to the user log data; the user satisfaction index data is index data of a combined dimension of a user mobile phone number dimension, a time dimension, a communication cell dimension and a communication base station dimension; establishing a user satisfaction index incidence matrix according to the user satisfaction index data; wherein, the matrix elements of the user satisfaction index incidence matrix comprise: user mobile phone number dimension, time dimension, communication cell dimension, communication base station dimension and user satisfaction index data.

Optionally, the user satisfaction emphasis index determining module 430 may be specifically configured to: acquiring target incidence matrix element data of a user satisfaction index incidence matrix; carrying out normalization processing on the element data of the target incidence matrix to obtain a user satisfaction index normalization incidence matrix; determining a user satisfaction index covariance matrix of the user satisfaction index normalized incidence matrix according to the user satisfaction index normalized incidence matrix; determining a user satisfaction index characteristic value according to the user satisfaction index covariance matrix; and sequencing the user satisfaction indexes according to the user satisfaction index characteristic values to determine at least one user satisfaction key index according to the user satisfaction index sequencing result.

Optionally, the highlight index satisfaction score determining module 450 may be specifically configured to:

acquiring user satisfaction key index data and user satisfaction key index time data of each user satisfaction key index in the user satisfaction key index incidence matrix; sorting the user satisfaction key indexes according to the user satisfaction key index data and the user satisfaction key index time data to obtain first limit index data and second limit index data of the user satisfaction key indexes; and determining the key index satisfaction degree score of each user satisfaction degree key index according to the first limit index data and the second limit index data.

Optionally, the highlight index satisfaction score determining module 450 may be further configured to: determining the user satisfaction key index service attribute of each user satisfaction key index; determining the key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index service attribute and the following formula:

f(x)＝(1-p)g(x)+(10-g(x))p

wherein x represents user satisfaction key index data of each user satisfaction key index; under the condition that the user satisfaction key index service attribute is determined to be a first service attribute, p is a first calculation parameter, and a is a second limit index data parameter; and under the condition that the user satisfaction degree key index service attribute is determined to be a second service attribute, p is a second calculation parameter, and a is a first limit index data parameter.

Optionally, the user satisfaction determining module 460 may be specifically configured to: determining the weight value of the key index of each user satisfaction degree key index according to the user satisfaction degree index incidence matrix and each user satisfaction degree key index; and determining the user satisfaction score according to the key index satisfaction score and the key index weight value of each user satisfaction key index.

The user satisfaction determining device provided by the embodiment of the invention can execute the user satisfaction determining method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method.

Example four

FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the user satisfaction determination method.

In some embodiments, the user satisfaction determination method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the user satisfaction determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the user satisfaction determination method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A user satisfaction determination method, comprising:

acquiring user log data of a user;

2. The method of claim 1, wherein the user log data comprises user log data; the user internet log data comprises user internet service log data and user internet signaling log data.

3. The method according to claim 1 or 2, wherein determining a user satisfaction index correlation matrix of user satisfaction indexes from the user log data comprises:

determining user satisfaction index data of at least one of the user satisfaction indexes according to the user log data; the user satisfaction index data is index data of a combined dimension of a user mobile phone number dimension, a time dimension, a communication cell dimension and a communication base station dimension;

establishing a user satisfaction index incidence matrix according to the user satisfaction index data; wherein the matrix elements of the user satisfaction index incidence matrix include: the user mobile phone number dimension, the time dimension, the communication cell dimension, the communication base station dimension, and the user satisfaction index data.

4. The method according to claim 3, wherein said determining at least one user satisfaction highlight indicator according to said user satisfaction indicator correlation matrix comprises:

acquiring target incidence matrix element data of the user satisfaction index incidence matrix;

carrying out normalization processing on the element data of the target incidence matrix to obtain a user satisfaction index normalization incidence matrix;

determining a user satisfaction index covariance matrix of the user satisfaction index normalized incidence matrix according to the user satisfaction index normalized incidence matrix;

determining a user satisfaction index characteristic value according to the user satisfaction index covariance matrix;

and sorting the user satisfaction index according to the user satisfaction index characteristic value to determine at least one user satisfaction key index according to a user satisfaction index sorting result.

5. The method according to claim 4, wherein determining a key indicator satisfaction score for each of the user satisfaction key indicators according to the user satisfaction key indicator correlation matrix comprises:

acquiring user satisfaction key index data and user satisfaction key index time data of each user satisfaction key index in the user satisfaction key index incidence matrix;

sequencing each user satisfaction degree key index according to each user satisfaction degree key index data and each user satisfaction degree key index time data to obtain first limit index data and second limit index data of each user satisfaction degree key index;

and determining the key index satisfaction degree score of each user satisfaction degree key index according to the first limit index data and the second limit index data.

6. The method of claim 5, wherein determining a highlight index satisfaction score for each of the user satisfaction highlight indices from the first and second boundary index data comprises:

determining the service attribute of the user satisfaction key index of each user satisfaction key index;

determining the key index satisfaction degree score of each user satisfaction degree key index according to the user satisfaction degree key index service attribute and the following formula:

f(x)＝(1-p)g(x)+(10-g(x))p

wherein x represents user satisfaction key index data of each user satisfaction key index; under the condition that the user satisfaction key index service attribute is determined to be a first service attribute, p is a first calculation parameter, and a is a second limit index data parameter; and under the condition that the user satisfaction key index service attribute is determined to be a second service attribute, p is a second calculation parameter, and a is the first limit index data parameter.

7. The method according to claim 1 or 2, further comprising, after said determining at least one user satisfaction highlight indicator according to said user satisfaction indicator correlation matrix:

determining a key index weight value of each user satisfaction key index according to the user satisfaction index incidence matrix and each user satisfaction key index;

determining a user satisfaction score according to each of the key index satisfaction scores, wherein the determining a user satisfaction score according to each of the key index satisfaction scores comprises:

and determining the user satisfaction degree score according to the key index satisfaction degree score and the key index weight value of each user satisfaction degree key index.

8. A user satisfaction determination apparatus, comprising:

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the user satisfaction determination method of any of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to perform the user satisfaction determination method of any of claims 1-7 when executed.