CN113891384A - Method, device, service terminal and medium for determining network quality matching degree - Google Patents
Method, device, service terminal and medium for determining network quality matching degree Download PDFInfo
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- CN113891384A CN113891384A CN202111264164.1A CN202111264164A CN113891384A CN 113891384 A CN113891384 A CN 113891384A CN 202111264164 A CN202111264164 A CN 202111264164A CN 113891384 A CN113891384 A CN 113891384A
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Abstract
The application provides a method, a device, a service terminal and a medium for determining network quality matching degree. The method is applied to the service terminal and comprises the following steps: acquiring network quality parameters uploaded by a user terminal of a user; determining the user category of the user based on a preset classification algorithm; and determining the network quality matching degree corresponding to the user matching degree according to the network quality parameters corresponding to the users and the user categories of the users. According to the method for determining the network quality matching degree, the network quality is evaluated by taking the user as a unit, the precision of the network quality evaluation is improved, meanwhile, the user is classified, and the network quality is evaluated based on the classification and the network quality parameters uploaded by the user terminal, so that the accuracy of the evaluation is improved.
Description
Technical Field
The present application relates to communications technologies, and in particular, to a method, an apparatus, a service terminal, and a medium for determining a network quality matching degree.
Background
With the popularization of intelligent terminals, more and more users use data services to perform data transmission or information interaction, such as watching videos, reading news and the like, thereby providing higher requirements for the network quality of mobile communication.
In order to evaluate the matching degree of the network quality of mobile communication, in the prior art, data of a BSS (Business Support System) domain and an OSS (Operation Support System) domain are generally fused to evaluate the regional overall matching degree, and the evaluation of the fine-grained matching degree for a user cannot be obtained, so that the evaluation precision of the matching degree is low, and the requirement cannot be met.
Disclosure of Invention
The application provides a method, a device, a service terminal and a medium for determining network quality matching degree, which are used for solving the problem of poor network matching degree evaluation precision.
In a first aspect, the present application provides a method for determining a network quality matching degree, where the method is applied to a service terminal, and includes:
acquiring network quality parameters uploaded by a user terminal of a user; determining the user category of the user based on a preset classification algorithm; and determining the network quality matching degree corresponding to the user matching degree according to the network quality parameters corresponding to the users and the user categories of the users.
Optionally, determining the network quality matching degree corresponding to the user according to the network quality parameter corresponding to the user and the user category of the user includes:
performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network quality parameter; and determining the network quality matching degree corresponding to the user based on the user category of the user and each transformed network quality parameter corresponding to the user.
Optionally, the network quality parameters include a first parameter and a second parameter, and performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network parameter, including:
performing data conversion on the first parameter corresponding to the user based on a first relational expression to obtain a converted first parameter; performing data conversion on the second parameter corresponding to the user based on a second relational expression to obtain a converted second parameter; wherein the first relational expression and the second relational expression are different relational expressions.
Optionally, both the first relational expression and the second relational expression are linear functions, and the slope and/or intercept in the first relational expression and the second relational expression are different.
Optionally, the determining, by the network quality parameter including a first parameter and a second parameter, a network quality matching degree corresponding to the user based on the user category of the user and each transformed network quality parameter corresponding to the user includes:
calculating a first matching degree based on the converted first parameter and a preset relational expression; calculating a second matching degree based on the converted second parameter and a preset relational expression; and determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user, and the second matching degree corresponding to the user includes:
determining the user weight of the user according to the user category of the user; and determining the network quality matching degree corresponding to the user according to the user weight of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, the preset relational expression is a Sigmoid function.
Optionally, the first parameter is a reference signal received power, and the second parameter is a signal to interference plus noise ratio.
Optionally, determining the user category of the user based on a preset classification algorithm includes:
acquiring user data and network service data of the user; and determining the user category of the user based on a preset classification algorithm, user data and network service data.
Optionally, the preset classification algorithm is a k-nearest neighbor algorithm.
Optionally, the method further includes:
and determining the value of k in the k-nearest neighbor algorithm according to the number of the user categories.
In a second aspect, the present application provides a device for determining a network quality matching degree, where the device is applied to a service terminal, and the device includes:
the parameter acquisition module is used for acquiring network quality parameters uploaded by a user terminal of a user; the category determination module is used for determining the user category of the user based on a preset classification algorithm; and the matching degree determining module is used for determining the network quality matching degree corresponding to the user according to the network quality parameters corresponding to the user and the user category of the user.
Optionally, the matching degree determining module includes:
the parameter transformation unit is used for carrying out parameter transformation on each network quality parameter corresponding to the user so as to obtain each transformed network quality parameter; and the matching degree determining unit is used for determining the matching degree of the network quality corresponding to the user based on the user category of the user and each transformed network quality parameter corresponding to the user.
Optionally, the network quality parameter includes a first parameter and a second parameter, and the parameter transformation unit is specifically configured to:
performing data conversion on the first parameter corresponding to the user based on a first relational expression to obtain a converted first parameter; performing data conversion on the second parameter corresponding to the user based on a second relational expression to obtain a converted second parameter; wherein the first relational expression and the second relational expression are different relational expressions.
Optionally, the network quality parameter includes a first parameter and a second parameter, and the matching degree determining unit includes:
the first calculating subunit is used for calculating a first matching degree based on the converted first parameter and a preset relational expression; the second calculating subunit is used for calculating a second matching degree based on the converted second parameter and a preset relational expression; and the matching degree determining subunit is used for determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, the matching degree determining subunit is specifically configured to:
determining the user weight of the user according to the user category of the user; and determining the network quality matching degree corresponding to the user according to the user weight of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, the category determining module is specifically configured to:
acquiring user data and network service data of the user; and determining the user category of the user based on a preset classification algorithm, user data and network service data.
Optionally, the preset classification algorithm is a k-nearest neighbor algorithm, and the apparatus further includes:
and the parameter determining module is used for determining the value of k in the k-nearest neighbor algorithm according to the number of the user categories.
In a third aspect, the present application further provides a service terminal, including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory to implement the method for determining the network quality matching degree provided by the first aspect of the present application.
In a fourth aspect, the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method for determining the network quality matching degree provided in the first aspect of the present application is implemented.
In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for determining the network quality matching degree provided in the first aspect of the present application is implemented.
According to the method, the device, the service terminal and the medium for determining the network quality matching degree, the network quality matching degree corresponding to each user is determined by combining the network quality data uploaded by the user terminal of each user and the user category, so that the network quality evaluation method based on the user with fine granularity is realized, and the accuracy and the comprehensiveness of the network quality evaluation are improved; meanwhile, the user categories are obtained by classifying the users in advance, and the matching degree is calculated based on the user categories and the network quality parameters corresponding to the users, so that the accuracy of network quality evaluation is 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 diagram of a communication system architecture according to an embodiment of the present application;
fig. 2 is a flowchart of a method for determining a network quality matching degree according to an embodiment of the present application;
fig. 3 is a flowchart of a method for determining a network quality matching degree according to another embodiment of the present application;
FIG. 4 is a flowchart of step S304 in the embodiment of FIG. 3 of the present application;
fig. 5 is a schematic structural diagram of a device for determining network quality matching degree according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a service terminal according to an embodiment of the present application.
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.
Fig. 1 is a schematic diagram of a communication system architecture according to an embodiment of the present application, and as shown in fig. 1, the communication system includes: a service terminal, an access network device and a plurality of user terminals, it is assumed that the plurality of user terminals includes user terminal 1, user terminal 2, user terminal 3 and user terminal 4 in the figure. It should be noted that the communication System shown in fig. 1 may be applicable to different network formats, for example, may be applicable to Global System for Mobile communication (GSM), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), Long Term Evolution (Long Term Evolution, LTE), and future 5G network formats. Optionally, the communication system may be a system in a scenario of high-Reliable and Low Latency Communications (URLLC) transmission in a 5G communication system.
Therefore, optionally, the access Network device may be a Base Station (BTS) and/or a Base Station Controller in GSM or CDMA, a Base Station (NodeB, NB) and/or a Radio Network Controller (RNC) in WCDMA, an evolved Node B (eNB or eNodeB) in LTE, or a relay Station or an access point, or a Base Station (gbb) in a future 5G Network, and the application is not limited herein.
The user terminal may be a wireless terminal or a wired terminal. A wireless terminal may refer to a device that provides voice and/or other traffic data connectivity to a user, a handheld device having wireless connection capability, or other processing device connected to a wireless modem. A wireless terminal, which may be a mobile terminal such as a mobile telephone (or "cellular" telephone) and a computer having a mobile terminal, for example, a portable, pocket, hand-held, computer-included, or vehicle-mounted mobile device, may communicate with one or more core Network devices via a Radio Access Network (RAN), and may exchange language and/or data with the RAN. For another example, the Wireless terminal may also be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), and other devices. A wireless Terminal may also be referred to as a system, a Subscriber Unit (Subscriber Unit), a Subscriber Station (Subscriber Station), a Mobile Station (Mobile), a Remote Station (Remote Station), a Remote Terminal (Remote Terminal), an Access Terminal (Access Terminal), a User Terminal (User Terminal), a User Agent (User Agent), and a User Device or User Equipment (User Equipment), which are not limited herein. Optionally, the user terminal may also be a smart watch, a tablet computer, or the like.
The service terminal may be a server, and is connected to the access network device to perform data communication with each user terminal through the access network device.
The method for determining the network quality matching degree provided by the application can be executed by the service terminal in the communication system. When the network quality needs to be evaluated, it is usually performed based on the data of the BSS domain and the OSS domain stored in the service terminal to obtain each index parameter for evaluating the network quality, so as to evaluate the network quality by using the base station or the cell as a unit to determine whether the network of the base station or the cell needs to be optimally adjusted. By adopting the method to evaluate the network quality, only regional evaluation results can be obtained, but evaluation results taking users as units cannot be obtained, and the evaluation precision is low.
The method for determining the network quality matching degree provided by the application aims to solve the technical problems in the prior art. The main conception is as follows: the method comprises the steps of classifying users based on a preset classification algorithm to obtain user categories of the users, determining network quality matching degrees corresponding to the users by combining the user categories and network quality parameters uploaded by user terminals of the users to determine whether the networks of the users are optimized and adjusted, achieving a network quality evaluation strategy with the users as the granularity, and being high in evaluation precision and comprehensiveness, and high in evaluation accuracy by combining the parameters and the user categories to perform network evaluation.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. 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. 2 is a flowchart of a method for determining a network quality matching degree according to an embodiment of the present application, where the method may be executed by a service terminal, and as shown in fig. 2, the method for determining a network quality matching degree includes the following steps:
step S201, obtaining a network quality parameter uploaded by a user terminal of a user.
The network quality parameter is a parameter for measuring network quality, such as a parameter for measuring LTE coverage and signal quality. The number of network Quality parameters may be one or more, and may include one or more of Signal to Interference plus Noise Ratio (SINR), Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (RSRQ), Received Signal Strength (RSSI), and the like.
Specifically, the service terminal may send a parameter acquisition request to the user terminal, and the user terminal uploads the network quality parameter based on the parameter acquisition request.
Specifically, the user can upload the network quality parameters, such as SINR and RSRP, to the cloud through an application program or an applet installed in the user terminal, and then the service terminal obtains the network quality parameters uploaded by each user terminal stored in the cloud, so as to obtain the network quality parameters corresponding to each user.
The network quality evaluation is carried out through each network quality parameter uploaded by the user terminal, so that the authenticity and reliability of the network quality evaluation are improved, and the perception condition of the user to the network can be reflected to a greater extent by the evaluation result.
Step S202, based on a preset classification algorithm, determining the user category of the user.
Specifically, the user categories with the preset number can be divided in advance, the preset number can be any positive integer greater than or equal to 3, the user categories can be divided in any mode, and the users with different user categories have different requirements on the network quality.
The preset classification algorithm may be a neural network-based classification algorithm, a k-nearest neighbor (k-NN) algorithm, or a decision tree algorithm, an isolated forest algorithm, or the like.
Specifically, the users may be classified based on the network service data corresponding to each user and/or the user data corresponding to the user, and the preset classification algorithm, so as to obtain the user category of each user.
In some embodiments, step S201 and step S202 may be executed in parallel, or step S202 is executed first and then step S201 is executed, and the execution sequence of step S201 and step S202 is not limited in the present application.
Step S203, determining the network quality matching degree corresponding to the user according to the network quality parameter corresponding to the user and the user category of the user.
Specifically, after determining the user category of the user and acquiring the network quality parameters uploaded by the user terminal of the user, the pre-designed correspondence relationship, the user category and the network quality parameters may be combined to calculate the network quality matching degree corresponding to the user, so as to evaluate whether the network of the user needs to be optimized and adjusted based on the network quality matching degree, and if so, optimization adjustment prompt information may be generated to prompt relevant personnel to formulate an optimization adjustment strategy of the network of the user.
The method for determining the network quality matching degree provided by this embodiment determines the network quality matching degree corresponding to each user by combining the network quality data uploaded by the user terminal of each user and the user category, thereby implementing a network quality evaluation method based on the user with a finer granularity, and improving the accuracy and comprehensiveness of network quality evaluation; meanwhile, the user categories are obtained by classifying the users in advance, and the matching degree is calculated based on the user categories and the network quality parameters corresponding to the users, so that the accuracy of network quality evaluation is improved.
Fig. 3 is a flowchart of a method for determining a network quality matching degree according to another embodiment of the present application, where this embodiment is further detailed in step S202 and step S203 on the basis of the embodiment shown in fig. 2, and as shown in fig. 3, the method for determining a network quality matching degree according to this embodiment may include the following steps:
step S301, acquiring network quality parameters, user data, and network service data uploaded by a user terminal of a user.
The user data may include the gender, the network access age, the user age, the common address, the mobile phone number, the ARPU (Average Monthly service income Per Unit) interval, and the like of the user, and the mobile phone number may be used as an index field to represent each user, so as to correspond the user to the corresponding matching degree one to one. The network service data refers to various data of network services performed by the user through the user terminal, and may include index parameters such as call times, call drop times, downlink RTT (Round Trip Time), video service blocking rate, page response average Time delay, and flows of various application programs, and may also include user complaint data such as communication quality complaint times, tariff dispute complaint times, service complaint times, and sensitive channel complaint times.
Specifically, the user data and the network service data corresponding to each user recorded in the BSS domain and the OSS domain may be obtained through the client ledger.
Specifically, a place where the user stays for a long time, such as a home address, can be selected, and the corresponding network quality parameters are used for network quality evaluation.
Step S302, based on a preset classification algorithm, user data and network service data, determining the user category of the user.
Specifically, the user category of each user may be determined from pre-dividing various user categories based on a preset classification algorithm, user data of each user, and network service data.
Further, after obtaining the user data of each user and the network service data of each user, the method further includes:
and preprocessing the user data and the network service data of each user to obtain the characteristic vector corresponding to each user.
Specifically, the preprocessing may include converting non-numerical data into numerical data, and may further include performing normalization processing on the numerical data, so as to map the numerical user data and the network service data into a value range of [0, 1 ].
For example, for the data of the gender of the user, the character of "male" may be mapped to 0, and the character of "female" may be mapped to 1.
For example, the formula used for normalization can be:
wherein, oldValue is data before normalization processing, min is the minimum value of the data, max is the maximum value of the data, and newValue is data after normalization processing.
In some embodiments, the feature vector of the preprocessed user may consist of a number of the following feature variables: the method comprises the steps of mobile phone number (y0), APRU interval (x1), network-accessing age group (x3), user gender (x4), microblog traffic (x5), instant messaging traffic (x6), music traffic (x7), video traffic (x8), page browsing traffic (x9), game traffic (x10), video service card-pause rate (x11), call times in a week (x12), call drop times in a week (x13), downlink RTT average time delay (x14), page response average time delay (x15), communication quality complaint times in three months (x16), tariff complaint times in three months (x17), service complaint times in three months (x18) and sensitive channel complaint times in three months (x 19).
Specifically, the mobile phone number may be set as an index field, so as to query the network quality matching degree corresponding to the user by inputting the mobile phone number of the user.
Further, after the feature vector corresponding to each user is obtained, the feature vector corresponding to each user is input into the model or platform corresponding to the preset classification algorithm, so that the user category of each user is obtained.
The users are classified based on the user data and the service network data, so that the network quality matching degree of the users is calculated by combining the user classes, and the adaptability and the accuracy of the matching degree calculation are improved.
Optionally, the preset classification algorithm is a k-nearest neighbor algorithm.
Specifically, the user category of each user is determined based on a k-proximity algorithm, a target data set of known user categories, and a feature vector of each user.
The target data set of the known category comprises feature vectors of users of which the user categories are known.
Further, for each user to be determined in the user category, calculating a distance between each feature vector in the target data set and the feature vector of the user, such as a euclidean distance, a manhattan distance, and the like, sorting each feature in the target data set according to an increasing order of the distances, selecting the user category corresponding to the feature vector sorted in the top k target data sets to form an alternative user category set, and determining the user category with the highest occurrence frequency as the user category of the user from the alternative category set.
In some embodiments, the value of k for the k-neighbor algorithm is 3.
And the user classification is carried out by adopting a k-proximity algorithm, so that the user classification efficiency is improved, and the user classification complexity is reduced.
Optionally, the method further includes:
and determining the value of k in the k-nearest neighbor algorithm according to the number of the user categories.
Specifically, a first corresponding relationship between the number of the user categories and the value of k may be pre-established, so that the value of k is determined based on the number of the divided user categories, that is, the preset number, and the first corresponding relationship.
Illustratively, k may take a value of 3 when the number of user categories is less than or equal to a first number, e.g., 4, and k may take a value of 5 when the number of user categories is greater than the first number and less than or equal to a second number, e.g., 10.
In some embodiments, k is an odd number greater than or equal to 3.
Further, an initial value of k can be determined based on the number of user categories, and then the value of k is adjusted through a training process of a preset classification algorithm.
By reasonably setting the value of k, the classification accuracy can be effectively improved, and under-fitting or over-fitting is avoided.
Further, before determining the user category of the user based on the preset classification algorithm, the user data and the network service data, a step of training the preset classification algorithm may be performed first, so as to determine the accuracy of the algorithm, the value of k and obtain the target data set through the training of the preset classification algorithm.
Specifically, a certain amount of sample data can be collected in advance, the sample data is divided into a training set and a verification set according to a ratio of 4:1, and a label is preset for each data in the training set and the verification set in an artificial mode or a mode of setting a function and judging conditions, wherein the label corresponding to the data is the user category of the user corresponding to the data, if the label includes A, B, C, D, if A represents a user with high requirement on network speed and sensitive to network perception, B represents a user with high requirement on network speed and sensitive to network perception, C represents a user with high requirement on network speed and insensitive to network perception, and D represents a user with low requirement on network speed and insensitive to network perception. Based on the preprocessing operation, preprocessing each data in the training set and the verification set to obtain each feature vector corresponding to the training set and the verification set, and setting an initial value of k, such as 3; calculating the distance between each feature vector in the verification set and each target vector by taking each feature vector in the training set as a target vector, obtaining k target vectors with the minimum distance from each feature vector in the verification set through sorting, determining a label with the highest occurrence frequency as an estimation label of each feature vector in the verification set from labels corresponding to the k target vectors with the minimum distance based on a voting method, further determining the accuracy of a k-nearest neighbor algorithm based on an actual label and the estimation label corresponding to each feature vector in the verification set, and taking each feature vector in the training set as a target data set of a known class or taking the feature vector in each verification set matched with the estimation label and each feature vector in the training set as the target data set of a known class if the accuracy is greater than or equal to a preset value, for subsequent user classification. And if the accuracy is smaller than the preset value, adjusting the value of k, and continuing the next round of training until the accuracy is larger than or equal to the preset value.
Step S303, performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network quality parameter.
Specifically, parameter transformation may be performed on each network quality parameter corresponding to each user through a preset function, such as a linear function or a nonlinear function, such as a proportional function, a positive correlation function, an exponential function, and the like.
Specifically, the preset functions corresponding to different network quality parameters may be different, for example, the parameters of the preset functions may be different, or the types of the preset functions may be different.
Optionally, the network quality parameters include a first parameter and a second parameter, and performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network parameter, including:
performing data conversion on the first parameter corresponding to the user based on a first relational expression to obtain a converted first parameter; performing data conversion on the second parameter corresponding to the user based on a second relational expression to obtain a converted second parameter; wherein the first relational expression and the second relational expression are different relational expressions.
And aiming at different network quality parameters, different relational expressions are set for data conversion of the network quality parameters, so that the converted network quality parameters can better reflect the corresponding relation between the network quality parameters and the network quality, and the accuracy of calculation of the network quality matching degree is further improved.
Specifically, the first parameter may be reference signal received power RSRP, and the second parameter may be signal to interference plus noise ratio SINR. When the RSRP is more than or equal to-80 dBm, the coverage rate is high, and the rating is good; when 80dBm > RSRP is more than or equal to-90 dBm, the coverage rate is moderate, and the rating is medium; when the RSRP is more than 90dBm and is more than or equal to-105 dBm, the coverage rate is general, the call drop rate is higher, and the rating is qualified; when the RSRP is less than or equal to-105 dBm, the coverage rate is poor, the service is basically unavailable, and the rating is poor. When the SINR is more than or equal to 15dBm, the grade is good; when 15dBm > SINR is more than or equal to 6dBm, the grade is medium; when the SINR is more than 6dBm and is more than or equal to 0dBm, the grade is qualified; when the SINR is ≦ 0dBm, the rating is poor. It can be seen that the rating standards of different network quality parameters are different, and in order to improve the accuracy of the network quality matching degree calculation, the network quality parameters need to be converted into parameters meeting the rating standards.
Step S304, determining the network quality matching degree corresponding to the user based on the user category of the user and the transformed network quality parameters corresponding to the user.
Optionally, fig. 4 is a flowchart of step S304 in the embodiment shown in fig. 3 of the present application, and as shown in fig. 4, step S304 may include the following steps:
step S3041, a first matching degree is calculated based on the converted first parameter and a preset relation.
Step S3042, a second matching degree is calculated based on the converted second parameter and the preset relation.
The preset relational expression can be any activation function, so that the first matching degree and the second matching degree are between 0 and 1.
In some embodiments, the step S3041 and the step S3042 may be executed in parallel, or the step S3041 is executed first and then the step S3042 is executed, or the step S3042 is executed first and then the step S3041 is executed, and the execution sequence of the step S3041 and the step S3042 is not limited in this application.
In some embodiments, the first parameter after conversion and the second parameter after conversion may be respectively substituted into the preset relational expression, so as to obtain the first matching degree and the second matching degree.
Optionally, the preset relational expression is a Sigmoid function.
And smoothly mapping each network quality parameter, namely the first parameter and the second parameter, to a value between [0, 1] through a Sigmoid function so as to facilitate the calculation of the subsequent network quality matching degree and the evaluation of the network quality.
Due to the self-characteristics of the Sigmoid function, in order to make the calculation of the first matching degree and the second matching degree conform to the rating standards of the first parameter and the second parameter, the first parameter and the second parameter need to be processed, such as linear transformation, so as to obtain the transformed first parameter and second parameter.
Optionally, the first relational expression and the second relational expression are both linear functions,and the slope and/or intercept in the first and second relationships are different. The first relation is: xRSRP_new＝K1*XRSRP_old+C1Wherein X isRSRP_newFor converted RSRP, XRSRP_oldIs the RSRP before conversion. The first relation is: xSINR_new＝K2*XSINR_old+C2Wherein X isSINR_newFor the converted SINR, XSINR_oldIs the SINR before the conversion.
Exemplary, K1＝0.4，C1＝37，K2＝2/3，C2＝-5。
Further, the first matching degree Q1 and the second matching degree Q2 can be obtained by substituting the converted first parameter and the converted second parameter into the Sigmoid function.
Step S3043, determining a network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user, and the second matching degree corresponding to the user.
Specifically, the user category may be digitized, and the network quality matching degree corresponding to the user may be determined based on a product of the user category, the first matching degree, and the second matching degree.
Optionally, determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user, and the second matching degree corresponding to the user includes:
determining the user weight of the user according to the user category of the user; and determining the network quality matching degree corresponding to the user according to the user weight of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Wherein, the higher the requirement level of the network quality corresponding to the user category is, the lower the corresponding weight is.
For example, if the users are sequentially classified into A, B and C user categories according to the demand level, the weight of the class a user may be 0.8, the weight of the class B user may be 1, and the weight of the class C user may be 1.2.
For example, if the users are classified into A, B, C, D and E five user categories according to the demand level, the weight of the a-type user may be 0.6, the weight of the B-type user may be 0.8, the weight of the C-type user may be 1, the weight of the D-type user may be 1.2, and the weight of the E-type user may be 1.4.
Specifically, after the user weight is determined, the product of the user weight of the user, the first matching degree corresponding to the user, and the second matching degree corresponding to the user is calculated, so that the network quality matching degree corresponding to the user can be obtained.
By reasonably setting the weights corresponding to the user categories, the different requirements of the users of different categories on the network quality are fully considered, and the accuracy of the calculation of the matching degree of the network quality is further improved.
Furthermore, after the network quality matching degree of each user is obtained, a network evaluation result can be output, wherein the network evaluation result comprises the mobile phone number of each user and the corresponding network quality matching degree, so that operation and maintenance personnel can quickly search the network quality matching degree of the user through the mobile phone number.
Further, operation and maintenance prompt information can be generated according to the user data, the network service data and the network quality matching degree corresponding to each user with the corresponding network quality matching degree lower than the preset threshold value, and the operation and maintenance prompt information is displayed or/transmitted to prompt operation and maintenance personnel to carry out optimization adjustment on the network of each user with the lower network quality matching degree.
In the embodiment, users are classified by combining user data and network service data of the users, so that the users are accurately classified according to network quality requirements; and then obtaining the network quality parameters uploaded by the user terminal, and performing data transformation on each network quality parameter, so that each transformed network quality parameter accords with the rating standard thereof, and further calculating the corresponding network quality matching degree of the user based on the user type and each transformed network quality parameter, thereby realizing the network quality evaluation with the user as the granularity, improving the progress of the network quality evaluation, and simultaneously performing the network evaluation by combining two factors of the user type and the parameter, improving the accuracy of the network quality evaluation, so that the determined matching degree accords with the real network perception of various users.
Fig. 5 is a schematic structural diagram of an apparatus for determining network quality matching degree according to an embodiment of the present application, and as shown in fig. 5, the apparatus is suitable for a service terminal, and the apparatus includes: a parameter obtaining module 510, a category determining module 520 and a matching degree determining module 530.
The parameter obtaining module 510 is configured to obtain a network quality parameter uploaded by a user terminal of a user; a category determining module 520, configured to determine a user category of the user based on a preset classification algorithm; a matching degree determining module 530, configured to determine a network quality matching degree corresponding to the user according to the network quality parameter corresponding to the user and the user category of the user.
Optionally, the matching degree determining module 530 includes:
the parameter transformation unit is used for carrying out parameter transformation on each network quality parameter corresponding to the user so as to obtain each transformed network quality parameter; and the matching degree determining unit is used for determining the matching degree of the network quality corresponding to the user based on the user category of the user and each transformed network quality parameter corresponding to the user.
Optionally, the network quality parameter includes a first parameter and a second parameter, and the parameter transformation unit is specifically configured to:
performing data conversion on the first parameter corresponding to the user based on a first relational expression to obtain a converted first parameter; performing data conversion on the second parameter corresponding to the user based on a second relational expression to obtain a converted second parameter; wherein the first relational expression and the second relational expression are different relational expressions.
Optionally, the network quality parameter includes a first parameter and a second parameter, and the matching degree determining unit includes:
the first calculating subunit is used for calculating a first matching degree based on the converted first parameter and a preset relational expression; the second calculating subunit is used for calculating a second matching degree based on the converted second parameter and a preset relational expression; and the matching degree determining subunit is used for determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, the matching degree determining subunit is specifically configured to:
determining the user weight of the user according to the user category of the user; and determining the network quality matching degree corresponding to the user according to the user weight of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
Optionally, the category determining module 520 is specifically configured to:
acquiring user data and network service data of the user; and determining the user category of the user based on a preset classification algorithm, user data and network service data.
Optionally, the preset classification algorithm is a k-nearest neighbor algorithm, and the apparatus further includes:
and the parameter determining module is used for determining the value of k in the k-nearest neighbor algorithm according to the number of the user categories.
The device for determining the network quality matching degree provided by the embodiment of the application can execute the method for determining the network quality matching degree provided by any embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method.
Fig. 6 is a schematic structural diagram of a service terminal according to an embodiment of the present application, and as shown in fig. 6, the service terminal includes: memory 610, processor 620, and computer programs.
The computer program is stored in the memory 610 and configured to be executed by the processor 620 to implement the method for determining the network quality matching degree provided by any of the embodiments corresponding to fig. 2 to 4 of the present application.
Wherein the memory 610 and the processor 620 are connected by a bus 630.
The related description may be understood by referring to the related description and effect corresponding to the steps in fig. 2 to fig. 4, and redundant description is not repeated here.
A non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a service terminal, enable the service terminal to perform the method for determining a network quality matching degree described above.
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.
Embodiments of the present application also provide a computer program product comprising an executable computer program, which is stored in a readable storage medium. The at least one processor of the service terminal may read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the apparatus for determining a network quality matching degree to implement the method for determining a network quality matching degree provided in the above-described various embodiments.
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 (14)
1. A method for determining network quality matching degree is characterized in that the method is applied to a service terminal, and the method comprises the following steps:
acquiring network quality parameters uploaded by a user terminal of a user;
determining the user category of the user based on a preset classification algorithm;
and determining the network quality matching degree corresponding to the user according to the network quality parameters corresponding to the user and the user category of the user.
2. The method of claim 1, wherein determining the network quality matching degree corresponding to the user according to the network quality parameter corresponding to the user and the user category of the user comprises:
performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network quality parameter;
and determining the network quality matching degree corresponding to the user based on the user category of the user and each transformed network quality parameter corresponding to the user.
3. The method of claim 2, wherein the network quality parameters include a first parameter and a second parameter, and performing parameter transformation on each network quality parameter corresponding to the user to obtain each transformed network parameter comprises:
performing data conversion on the first parameter corresponding to the user based on a first relational expression to obtain a converted first parameter;
performing data conversion on the second parameter corresponding to the user based on a second relational expression to obtain a converted second parameter;
wherein the first relational expression and the second relational expression are different relational expressions.
4. The method of claim 3, wherein the first and second relationships are both linear functions, and wherein the first and second relationships differ in slope and/or intercept.
5. The method of claim 2, wherein the network quality parameters include a first parameter and a second parameter, and determining the network quality matching degree corresponding to the user based on the user category of the user and the transformed network quality parameters corresponding to the user comprises:
calculating a first matching degree based on the converted first parameter and a preset relational expression;
calculating a second matching degree based on the converted second parameter and a preset relational expression;
and determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
6. The method of claim 5, wherein determining the network quality matching degree corresponding to the user according to the user category of the user, the first matching degree corresponding to the user, and the second matching degree corresponding to the user comprises:
determining the user weight of the user according to the user category of the user;
and determining the network quality matching degree corresponding to the user according to the user weight of the user, the first matching degree corresponding to the user and the second matching degree corresponding to the user.
7. The method of claim 5, wherein the predetermined relationship is a Sigmoid function.
8. The method according to any of claims 3-7, wherein the first parameter is reference signal received power and the second parameter is signal to interference plus noise ratio.
9. The method according to any one of claims 1-7, wherein determining the user category of the user based on a preset classification algorithm comprises:
acquiring user data and network service data of the user;
and determining the user category of the user based on a preset classification algorithm, user data and network service data.
10. The method of claim 9, wherein the predetermined classification algorithm is a k-nearest neighbor algorithm.
11. The method of claim 10, further comprising:
and determining the value of k in the k-nearest neighbor algorithm according to the number of the user categories.
12. A device for determining network quality matching degree, the device being applied to a service terminal, the device comprising:
the parameter acquisition module is used for acquiring network quality parameters uploaded by a user terminal of a user;
the category determination module is used for determining the user category of the user based on a preset classification algorithm;
and the matching degree determining module is used for determining the network quality matching degree corresponding to the user according to the network quality parameters corresponding to the user and the user category of the user.
13. A service terminal, 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 determining network quality match as claimed in any one of claims 1 to 11.
14. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, are configured to implement the method for determining network quality matching according to any one of claims 1 to 11.
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