CN116346697B - Communication service quality evaluation method and device and electronic equipment - Google Patents
Communication service quality evaluation method and device and electronic equipment Download PDFInfo
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- H—ELECTRICITY
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- H04W28/0231—Traffic management, e.g. flow control or congestion control based on communication conditions
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Abstract
The embodiment of the application provides a service quality evaluation method, a service quality evaluation device, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The method comprises the following steps: obtaining data to be tested of a target service; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for evaluating service quality, an electronic device, and a computer readable storage medium.
Background
With the continuous development of communication technology, communication services make the work and life of users more convenient. For example, in a practical scenario, the communication traffic includes data traffic, voice traffic, and the like; data services such as video browsing service, web browsing service, instant messaging service, game service, payment service, etc.; voice services such as session initiation protocol (Session initialization Protocol, SIP) telephony, etc., SIP protocol telephony such as dialing carrier customer service telephones, etc.
The evaluation of the service quality of the communication service is beneficial to improving the service quality, however, in the related art, the accuracy of the service quality evaluation is lower due to the limitation of the evaluation data or the evaluation mode.
Disclosure of Invention
The object of the present application is to solve at least one of the above-mentioned technical drawbacks, in particular the technical drawback of low accuracy of the quality of service evaluation.
According to an aspect of the present application, there is provided a service quality evaluation method, including: acquiring data to be tested of a target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes;
the data to be detected are predicted through a preset predictor, and a predicted result of the target service is obtained;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters include dial test data of the sample service.
Optionally, before the obtaining the data to be tested of the target service, the method further includes:
Acquiring sample data;
inputting sample service parameters in the sample data into an initial model to obtain a sample prediction result corresponding to each training sample;
determining a training loss value according to the sample prediction result and the first reference result;
and based on the training loss value, repeating training on the initial model until the preset predictor meeting the training ending condition is obtained.
Optionally, the acquiring sample data includes:
acquiring initial service parameters of at least one sample service;
and screening the initial service parameters, and determining the sample service parameters of which the weight values meet preset conditions in the initial service parameters.
Optionally, the screening the initial service parameter, determining the sample service parameter with the weight value meeting the preset condition in the initial service parameter includes:
calculating the association degree between at least two initial service parameters;
taking the initial service parameters with the association degree smaller than a first preset value as first screening parameters;
determining the sample service parameters of which the weight values meet prior conditions in the first screening parameters; wherein the prior condition comprises a preset condition parameter.
Optionally, the calculating the association degree between at least two initial service parameters includes:
calculating the association degree according to the initial service parameters and the first data relationship;
wherein the first data relationship comprises:
；
wherein ,representing the degree of association;
representing said initial traffic parameter->Is the i-th value of (2); />Representation->Average value of (2); />The i-th value of said initial traffic parameter y, is->Represents the average value of y.
Optionally, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the method further includes:
dividing the value range of the first screening parameter into at least two numerical intervals;
and determining the weight value according to the number of positive samples in the interval, the number of negative samples in the interval, the total number of positive samples, the total number of negative samples and the second data relationship corresponding to each numerical interval.
Optionally, the second data relationship includes:
wherein ,representing the weight value;
representing the number of positive samples in the interval; />Representing the total number of positive samples; />Representing the number of negative samples of the interval; />Representing the total number of negative samples; />Representing the number of said numerical intervals; / >Representation pair->Taking the logarithm.
Optionally, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the method further includes:
screening a second screening parameter with a weight value larger than a second preset value in the first screening parameter;
the determining the sample service parameter with the weight value meeting the prior condition in the first screening parameter comprises the following steps:
and determining the sample service parameters meeting prior conditions in the second screening parameters.
Optionally, after the obtaining the prediction result of the target service, the method further includes:
determining a second reference result;
and determining a comprehensive prediction result according to the second reference result and at least one prediction result corresponding to the target service.
Optionally, the determining the comprehensive prediction result according to the second reference result and at least one prediction result corresponding to the target service includes:
determining weight data corresponding to the second reference result and the prediction result respectively;
and determining the comprehensive prediction result according to the second reference result, the prediction result and weight data corresponding to the second reference result and the prediction result respectively.
According to another aspect of the present application, there is provided a service quality evaluation apparatus, including:
the data acquisition module is used for acquiring the data to be detected of the target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes;
the prediction module is used for performing prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters include dial test data of the sample service.
According to another aspect of the present application, there is provided an electronic device including:
a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the method for quality of service evaluation according to any one of the first aspects of the application.
For example, a third aspect of the present application provides a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus;
The memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform operations corresponding to the quality of service evaluation method according to the first aspect of the present application.
According to a further aspect of the present application there is provided a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the quality of service evaluation method according to any of the first aspects of the present application.
For example, in a fourth aspect of the embodiment of the present application, there is provided a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the service quality evaluation method shown in the first aspect of the present application.
According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from a computer-readable storage medium by a processor of a computer device, the computer instructions being executed by the processor to cause the computer device to perform the methods provided in the various alternative implementations of the first or second aspect described above.
The technical scheme provided by the application has the beneficial effects that:
in the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are required to be used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic diagram of a system architecture of a method for evaluating service quality according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for evaluating service quality according to an embodiment of the present application;
fig. 3 is a second flow chart of a method for evaluating quality of service according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a service quality evaluation device according to an embodiment of the present application;
Fig. 5 is a schematic structural diagram of an electronic device for evaluating service quality according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described below with reference to the drawings in the present application. It should be understood that the embodiments described below with reference to the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application, and the technical solutions of the embodiments of the present application are not limited.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, information, data, steps, operations, elements, and/or components, but do not preclude the presence or addition of other features, information, data, steps, operations, elements, components, and/or groups thereof, all of which may be included in the present specification. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein indicates that at least one of the items defined by the term, e.g., "a and/or B" may be implemented as "a", or as "B", or as "a and B".
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.
At least part of the content in the service quality evaluation method provided by the embodiment of the application relates to the fields of machine learning and the like in the artificial intelligence field, and also relates to various fields of Cloud technology, such as Cloud computing in Cloud technology (Cloud technology), cloud service and related data computing processing in the big data field.
Artificial intelligence (Artificial Intelligence, AI for short) is a theory, method, technique, and application system that simulates, extends, and extends human intelligence using a digital computer or a machine controlled by a digital computer, perceives the environment, obtains knowledge, and uses the knowledge to obtain optimal results. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision.
The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Machine Learning (ML) is a multi-domain interdisciplinary, involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Machine learning is the core of artificial intelligence, a fundamental approach to letting computers have intelligence, which is applied throughout various areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.
In order to further explain the technical solution provided by the embodiments of the present application, the following details are described with reference to the accompanying drawings and the detailed description. Although embodiments of the present application provide the method operational steps shown in the following embodiments or figures, more or fewer operational steps may be included in the method based on routine or non-inventive labor. In steps where there is logically no necessary causal relationship, the execution order of the steps is not limited to the execution order provided by the embodiments of the present application.
First, fig. 1 is a system architecture diagram of a service quality evaluation method according to an embodiment of the present application. The system may comprise a server 101 and a cluster of terminals, wherein the server 101 may be considered as a background server for the evaluation process.
The terminal cluster may include: terminal 102, terminal 103, terminals 104, … …, wherein a client supporting the evaluation process may be installed in the terminals. There may be a communication connection between terminals, for example, between terminal 102 and terminal 103, and between terminal 103 and terminal 104.
Meanwhile, the server 101 may provide services for the terminal cluster through a communication connection function, and any terminal in the terminal cluster may have a communication connection with the server 101, for example, a communication connection exists between the terminal 102 and the server 101, and a communication connection exists between the terminal 103 and the server 101, where the above communication connection is not limited to a connection manner, and may be directly or indirectly connected through a wired communication manner, may also be directly or indirectly connected through a wireless communication manner, or may also be other manners.
The network of communication connections may be wide area networks or local area networks, or a combination of both. The application is not limited in this regard.
The service quality evaluation method of the embodiment of the application can be executed on the server side or the terminal side, and the execution main body is not limited in the embodiment of the application.
The method provided by the embodiment of the present application may be performed by a computer device, including but not limited to a terminal (including the above-described user terminal as well) or a server (including the above-described server 101 as well). The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms. The terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the present application is not limited herein.
Of course, the method provided by the embodiment of the present application is not limited to the application scenario shown in fig. 1, but may be used in other possible application scenarios, and the embodiment of the present application is not limited. The functions that can be implemented by each device in the application scenario shown in fig. 1 will be described together in the following method embodiments, which are not described in detail herein.
The embodiment of the application provides a possible implementation manner, and the scheme can be executed by any electronic equipment, and optionally, any electronic equipment can be a server equipment with service quality evaluation capability or a device or a chip integrated on the equipment. Fig. 2 is a schematic flow chart of a method for evaluating service quality according to an embodiment of the present application, where the method includes the following steps:
step S201: acquiring data to be tested of a target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes.
The embodiment of the application can be applied to an application scene for evaluating the target service of a communication network, for example, the communication network can be a fifth generation mobile communication technology (5th Generation Mobile Communication Technology,5G) network.
The target service may include a service of a preset category and/or a preset application scenario. The preset category may include data service, voice service, etc.; data services such as video browsing service, web browsing service, instant messaging service, game service, payment service, etc.; voice services such as session initiation protocol (Session initialization Protocol, SIP) telephony, etc., SIP protocol telephony such as dialing carrier customer service telephones, etc. The preset application scene may include a high-frequency activity scene of a user using a communication network, for example, the preset application scene may include an application scene of a subway, a hotel, a mall, an underground parking lot, a high-rise office building, a residential building, and the like. The application scene can be divided into a good point, a middle point and a difference point according to the quality of the communication environment; the specific division criteria are shown in the following table 1:
Table 1:
in table 1, SS-RSRP represents the synchronization signal reference signal received power (Synchronization Signal Reference Signal Received Power), which is a linear average of the received secondary synchronization signal level.
SS-SINR represents the signal-to-interference plus noise ratio (Synchronization Signal Signal to Interference plus Noise Ratio), which is the ratio of the strength of the received useful signal to the strength of the received interfering signal (noise and interference).
SS-RSRQ (Synchronization Signal Reference Signal Receiving Quality) represents synchronization signal reference signal reception quality, which is a quantitative measure of the received synchronization signal (Synchronization Signal, SS) or channel state information (Channel State Information, CSI) reference signal.
The data to be tested comprises service parameters of the target service. Wherein, the service parameters may include performance parameters under the target service.
As an example, the service parameters may include one or more of the parameters in table 2:
table 2:
step S202: the data to be detected are predicted through a preset predictor, and a predicted result of the target service is obtained;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters include dial test data of the sample service.
Optionally, the prediction result is used for representing the predicted quality of the target service; for example, the prediction result may be a quality score of a target service or satisfaction of a user with the target service.
In the embodiment of the application, the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the first reference result may be tag data, for example, the first reference result may be the actual quality score or actual satisfaction degree of the user to the target service.
In an actual implementation scenario, the sample data may be obtained by dial testing a sample service, for example, the sample service may include a data service, a voice service, and the like; wherein, the data service such as video browsing service, web browsing service, instant communication service, game service, payment service, etc.; voice services such as session initiation protocol (Session initialization Protocol, SIP) telephony, etc., SIP protocol telephony such as dialing carrier customer service telephones, etc.
In the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
In one embodiment of the present application, before the obtaining the data to be tested of the target service, the method further includes:
acquiring sample data;
inputting sample service parameters in the sample data into an initial model to obtain a sample prediction result corresponding to each training sample;
determining a training loss value according to the sample prediction result and the first reference result;
and based on the training loss value, repeating training on the initial model until the preset predictor meeting the training ending condition is obtained.
In the embodiment of the application, before the data to be tested of the target service is obtained, the method further comprises the step of training the initial model to obtain a preset predictor.
Alternatively, a logistic regression algorithm may be used for model training. Logistic regression is achieved by a method of maximizing likelihood functions, gradient descent is used for solving parameters, value fields are mapped onto probability spaces by using Sigmoid functions on the basis of linear regression, and therefore probability values for distinguishing two classifications are obtained, and the formula of the Sigmoid functions is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Where x is a calculated value obtained by linear regression and P (Y) is a finally obtained probability value.
Optionally, in an actual implementation scenario, the sample service may be measured by a terminal device to obtain sample data. For example, the dial-up test can be performed in a high-frequency activity scene of the 5G communication network, and optionally, the high-frequency activity scene can include subways, hotels, large malls, underground parking lots, high-rise office buildings, residential buildings and the like; in addition, according to the quality of the communication environment, the movable scene can be divided into measured points, measured midpoint and measured difference point; specific division criteria may be shown in table 1 above, and are not described herein; alternatively, when dialing is performed, the duty ratio of the dial testing point may be set to 20%, the duty ratio of the dial testing midpoint may be set to 20%, and the duty ratio of the dial testing difference point may be set to 60%.
As an example, when dialing is performed, basic information of the dialing may be recorded, for example, the basic information may be as shown in the following table 3:
table 3:
wherein, optionally, the reference result of the dial test may include a reference quality score of the sample service or a reference satisfaction score of the sample service. Alternatively, the reference result may be determined based on the evaluation criteria of table 4 below, wherein the quality score is determined to be low or the satisfaction score is determined to be low if the dial test has the following phenomenon.
Table 4:
in one embodiment of the present application, the acquiring sample data includes:
acquiring initial service parameters of at least one sample service;
and screening the initial service parameters, and determining the sample service parameters of which the weight values meet preset conditions in the initial service parameters.
Optionally, when sample data is acquired, an initial service parameter may be acquired when the sample service is dialed, and the sample service parameter in the initial service parameter is selected by performing screening processing on the initial service parameter.
The initial service parameters may include some or all of the parameters shown in table 2.
In an actual implementation scenario, since there is a difference between the dimension and the magnitude of the initial service parameters, for example, some of the magnitude of the initial service parameters is smaller than 1, and some of the magnitude of the initial service parameters is larger than 1000, etc., the initial service parameters may be normalized first, so as to eliminate the influence of the magnitude difference. Optionally, the initial service parameters may be normalized by the following data relationship:
wherein ,values representing the j-th initial service parameter of the i-th sample after normalization, +.>Original value of the jth initial traffic parameter representing the ith sample,/for the sample>Representing the maximum value of the jth initial traffic parameter,representing the minimum value of the jth initial traffic parameter.
In one embodiment of the present application, when the initial service parameters are screened, a degree of association between at least two of the initial service parameters may be calculated;
taking the initial service parameters with the association degree smaller than a first preset value as first screening parameters;
determining the sample service parameters of which the weight values meet prior conditions in the first screening parameters; wherein the prior condition comprises a preset condition parameter.
Wherein in one embodiment of the present application, the calculating the association degree between at least two initial service parameters includes:
calculating the association degree according to the initial service parameters and the first data relationship;
wherein the first data relationship comprises:
；
wherein ,representing initial traffic parameters +.>Correlation with initial business parameter y;
representing said initial traffic parameter->Is the i-th value of (2); />Representation->Average value of (2); / >The i-th value of said initial traffic parameter y, is->Represents the average value of y.
In an actual implementation scenario, the degree of association may be referred to as pearson correlation coefficient; if the pearson correlation coefficient between the two initial service parameters is smaller than a first preset value, taking the two initial service parameters as the first screening parameters; if the pearson correlation coefficient between the two initial service parameters is greater than a first preset value, one of the initial service parameters may be deleted, and the other initial service parameter is taken as the first screening parameter.
In one embodiment of the present application, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the method further includes:
dividing the value range of the first screening parameter into at least two numerical intervals;
and determining the weight value according to the number of positive samples in the interval, the number of negative samples in the interval, the total number of positive samples, the total number of negative samples and the second data relationship corresponding to each numerical interval.
Optionally, when determining the weight value, the value range of the first filtering parameter may be first divided into at least two value intervals, that is, the binning operation, for example, the value range of the first filtering parameter may be divided into 20 intervals with approximately the same interval length by adopting an equal frequency bin splitting mode.
And then, determining the weight value according to the number of positive samples in the interval and the number of negative samples in the interval corresponding to each numerical interval, the total number of positive samples, the total number of negative samples and the second data relation.
Wherein the second data relationship comprises:
wherein ,representing the weight value;
representing the number of positive samples in the interval; />Representing the total number of positive samples; />Representing the number of negative samples of the interval; />Representing the total number of negative samples; />Representing the number of said numerical intervals; />Representation pair->Taking the logarithm.
Specifically, the positive sample may be an end user who is satisfied with the sample service dial test; the negative sample may be an end user who is dissatisfied with the sample traffic dial test.
In an actual implementation scenario, the aboveWhich may be denoted WOE (weight of evidence), evidence weight, WOE is a way of encoding traffic parameters.
The weight value IV (Information Value) is used for representing the contribution degree of the service parameter, namely the prediction capability of the service parameter, the value range of the IV value is [0, positive infinity), and generally, the higher the IV value is, the stronger the prediction capability of the service parameter is, and the higher the information contribution degree is.
In one embodiment of the present application, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the method further includes:
screening a second screening parameter with a weight value larger than a second preset value in the first screening parameter;
the determining the sample service parameter with the weight value meeting the prior condition in the first screening parameter comprises the following steps:
and determining the sample service parameters meeting prior conditions in the second screening parameters.
Alternatively, in some implementation scenarios, a first screening parameter with a weight value greater than 0.01 may be screened out as a second screening parameter. The sample traffic parameters in the second screening parameters may then be screened in combination with a priori conditions.
In an actual scenario, the prior condition may include a preset condition parameter determined by combining with expert experience, that is, when the sample service parameter is screened, the sample service parameter meeting the requirement of the preset condition parameter may be screened.
In one embodiment of the present application, after the obtaining the predicted quality score of the first network service, the method further includes:
determining a second reference result;
And determining a comprehensive prediction result according to the second reference result and at least one prediction result corresponding to the target service.
In an actual implementation scenario, the second reference result may be determined by a user characteristic of the dialed end user, such as a user gender, a user age, a user service usage duration, and the like.
In one embodiment of the present application, the determining the comprehensive prediction result according to the second reference result and the prediction result corresponding to at least one target service includes:
determining weight data corresponding to the second reference result and the prediction result respectively;
and determining the comprehensive prediction result according to the second reference result, the prediction result and weight data corresponding to the second reference result and the prediction result respectively.
Alternatively, the comprehensive prediction result may be expressed as。
wherein ,representing the comprehensive prediction result; />Representing the number of target services; />Representing the weight data;representing the prediction result.
In some alternative embodiments, the weight data may be determined by the following data relationship:. wherein ,/>Representing the weight data; />Representing an information utility value; / >；/>Representing information entropy; />The method comprises the steps of carrying out a first treatment on the surface of the Wherein k is>0, typically k=1/ln (m), where m represents the number of samples.
The implementation flow of model construction according to the embodiment of the present application is described below with reference to fig. 3:
step 31: firstly, designing a dial testing scheme; step 32: organizing dial testing to obtain a data set, wherein the data set is the initial service parameter of the embodiment of the application; step 33: screening the data set to obtain key indexes and weights thereof; step 34: expert experience corrects the key index set; step 35: and constructing a perception evaluation model, namely a preset predictor.
In the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
The embodiment of the present application provides a service quality evaluation device, as shown in fig. 4, the service quality evaluation device 40 may include: a data acquisition module 401, and a prediction module 402, wherein,
A data acquisition module 401, configured to acquire data to be tested of a target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes;
the prediction module 402 is configured to perform prediction processing on the data to be detected through a preset predictor, so as to obtain a prediction result of the target service;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters include dial test data of the sample service.
In one embodiment of the present application, the apparatus further includes acquiring sample data before the acquiring the data to be tested of the target service;
inputting sample service parameters in the sample data into an initial model to obtain a sample prediction result corresponding to each training sample;
determining a training loss value according to the sample prediction result and the first reference result;
and based on the training loss value, repeating training on the initial model until the preset predictor meeting the training ending condition is obtained.
In one embodiment of the present application, the training module is specifically configured to:
acquiring initial service parameters of at least one sample service;
and screening the initial service parameters, and determining the sample service parameters of which the weight values meet preset conditions in the initial service parameters.
In one embodiment of the present application, the training module is specifically configured to:
calculating the association degree between at least two initial service parameters;
taking the initial service parameters with the association degree smaller than a first preset value as first screening parameters;
determining the sample service parameters of which the weight values meet prior conditions in the first screening parameters; wherein the prior condition comprises a preset condition parameter.
In one embodiment of the present application, the training module is specifically configured to: calculating the association degree according to the initial service parameters and the first data relationship;
wherein the first data relationship comprises:
；
wherein ,representing the degree of association;
representing said initial traffic parameter->Is the i-th value of (2); />Representation->Average value of (2); />The i-th value of said initial traffic parameter y, is->Represents the average value of y.
In one embodiment of the present application, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the determining further includes:
Dividing the value range of the first screening parameter into at least two numerical intervals;
and determining the weight value according to the number of positive samples in the interval, the number of negative samples in the interval, the total number of positive samples, the total number of negative samples and the second data relationship corresponding to each numerical interval.
In one embodiment of the application, the second data relationship comprises:
wherein ,representing the weight value;
representing the number of positive samples in the interval; />Representing the total number of positive samples; />Representing the number of negative samples of the interval; />Representing the total number of negative samples; />Representing the number of said numerical intervals; />Representation pair->Taking the logarithm.
In one embodiment of the present application, before the determining the sample service parameter in which the weight value in the first screening parameter meets the prior condition, the determining further includes:
screening a second screening parameter with a weight value larger than a second preset value in the first screening parameter;
the determining the sample service parameter with the weight value meeting the prior condition in the first screening parameter comprises the following steps:
and determining the sample service parameters meeting prior conditions in the second screening parameters.
In one embodiment of the present application, the apparatus further includes a determining module configured to determine a second reference result after the obtaining of the predicted quality score of the first network traffic;
And determining a comprehensive prediction result according to the second reference result and at least one prediction result corresponding to the target service.
In one embodiment of the present application, the determining module is specifically configured to:
determining weight data corresponding to the second reference result and the prediction result respectively;
and determining the comprehensive prediction result according to the second reference result, the prediction result and weight data corresponding to the second reference result and the prediction result respectively.
The device of the embodiment of the present application may perform the method provided by the embodiment of the present application, and its implementation principle is similar, and actions performed by each module in the device of the embodiment of the present application correspond to steps in the method of the embodiment of the present application, and detailed functional descriptions of each module of the device may be referred to the descriptions in the corresponding methods shown in the foregoing, which are not repeated herein.
In the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
The embodiment of the application provides electronic equipment, which comprises: a memory and a processor; at least one program stored in the memory for execution by the processor, which, when executed by the processor, performs: in the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
In an alternative embodiment, there is provided an electronic device, as shown in fig. 5, the electronic device 4000 shown in fig. 5 includes: a processor 4001 and a memory 4003. Wherein the processor 4001 is coupled to the memory 4003, such as via a bus 4002. Optionally, the electronic device 4000 may further comprise a transceiver 4004, the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data, etc. It should be noted that, in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 is not limited to the embodiment of the present application.
The processor 4001 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 4001 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 4002 may include a path to transfer information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Memory 4003 may be, but is not limited to, ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory ), CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 4003 is used for storing application program codes (computer programs) for executing the present application and is controlled to be executed by the processor 4001. The processor 4001 is configured to execute application program codes stored in the memory 4003 to realize what is shown in the foregoing method embodiment.
Among them, electronic devices include, but are not limited to: mobile phones, notebook computers, multimedia players, desktop computers, etc.
Embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above.
In the embodiment of the application, the data to be detected of the target service is obtained; and carrying out prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service. The preset predictor is obtained by training dial testing data obtained by dial testing of sample service under different service types and application scenes, so that prediction is performed by combining service data under actual service and application scenes, the prediction accuracy of the preset predictor is improved, and the accuracy of service quality evaluation is improved.
The terms "first," "second," "third," "fourth," "1," "2," and the like in the description and in the claims and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate, such that the embodiments of the application described herein may be implemented in other sequences than those illustrated or otherwise described.
It should be understood that, although various operation steps are indicated by arrows in the flowcharts of the embodiments of the present application, the order in which these steps are implemented is not limited to the order indicated by the arrows. In some implementations of embodiments of the application, the implementation steps in the flowcharts may be performed in other orders as desired, unless explicitly stated herein. Furthermore, some or all of the steps in the flowcharts may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or phases may be performed at the same time, or each of these sub-steps or phases may be performed at different times, respectively. In the case of different execution time, the execution sequence of the sub-steps or stages can be flexibly configured according to the requirement, which is not limited by the embodiment of the present application.
The foregoing is merely an optional implementation manner of some of the implementation scenarios of the present application, and it should be noted that, for those skilled in the art, other similar implementation manners based on the technical ideas of the present application are adopted without departing from the technical ideas of the scheme of the present application, and the implementation manner is also within the protection scope of the embodiments of the present application.
Claims (7)
1. A method for evaluating quality of communication service, comprising:
acquiring data to be tested of a target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes;
the data to be detected are predicted through a preset predictor, and a predicted result of the target service is obtained;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters comprise dial test data of the sample service;
before the obtaining the data to be tested of the target service, the method further comprises:
acquiring sample data;
inputting sample service parameters in the sample data into an initial model to obtain a sample prediction result corresponding to each training sample;
determining a training loss value according to the sample prediction result and the first reference result;
repeating training on the initial model based on the training loss value until the preset predictor meeting the training ending condition is obtained;
the acquiring sample data includes:
Acquiring initial service parameters of at least one sample service;
screening the initial service parameters, and determining the sample service parameters of which the weight values meet preset conditions in the initial service parameters;
the step of screening the initial service parameters, the step of determining the sample service parameters with the weight values meeting the preset conditions in the initial service parameters comprises the following steps:
calculating the association degree between at least two initial service parameters;
taking the initial service parameters with the association degree smaller than a first preset value as first screening parameters;
determining the sample service parameters of which the weight values meet prior conditions in the first screening parameters; wherein the prior condition comprises preset condition parameters;
the calculating the association degree between at least two initial service parameters comprises the following steps:
calculating the association degree according to the initial service parameters and the first data relationship;
wherein the first data relationship comprises:
；
wherein ,representing the degree of association;
representing said initial traffic parameter->Is the i-th value of (2); />Representation->Average value of (2); />The i-th value of said initial traffic parameter y, is->Represents the average value of y;
Before the determining the sample service parameters of which the weight values in the first screening parameters meet the prior conditions, the method further comprises:
dividing the value range of the first screening parameter into at least two numerical intervals;
determining the weight value according to the number of positive samples in the interval, the number of negative samples in the interval, the total number of positive samples, the total number of negative samples and the second data relationship corresponding to each numerical interval;
the second data relationship comprises:
wherein ,representing the weight value;
representing the number of positive samples in the interval; />Representing the total number of positive samples; />Representing the number of negative samples of the interval;representing the total number of negative samples; />Representing the number of said numerical intervals; />Representation pair->Taking the logarithm.
2. The method for evaluating communication service quality according to claim 1, wherein before determining the sample service parameter in which the weight value in the first screening parameter satisfies the prior condition, the method further comprises:
screening a second screening parameter with a weight value larger than a second preset value in the first screening parameter;
the determining the sample service parameter with the weight value meeting the prior condition in the first screening parameter comprises the following steps:
And determining the sample service parameters meeting prior conditions in the second screening parameters.
3. The method for evaluating the quality of communication service according to claim 1, wherein after the obtaining the prediction result of the target service, the method further comprises:
determining a second reference result;
and determining a comprehensive prediction result according to the second reference result and at least one prediction result corresponding to the target service.
4. The method for evaluating quality of communication service according to claim 3, wherein determining the comprehensive prediction result according to the second reference result and the prediction result corresponding to at least one target service comprises:
determining weight data corresponding to the second reference result and the prediction result respectively;
and determining the comprehensive prediction result according to the second reference result, the prediction result and weight data corresponding to the second reference result and the prediction result respectively.
5. A communication service quality evaluation apparatus, comprising:
the data acquisition module is used for acquiring the data to be detected of the target service; the data to be tested comprise service parameters of target service of preset categories and/or preset application scenes;
The prediction module is used for performing prediction processing on the data to be detected through a preset predictor to obtain a prediction result of the target service;
the preset predictor is obtained by training according to sample data of sample service; the sample data comprises sample service parameters and first reference results corresponding to each group of sample service parameters; the sample service parameters comprise dial test data of the sample service;
the device also comprises a training module, which is used for acquiring the data to be tested of the target service before the data to be tested of the target service is acquired,
acquiring sample data;
inputting sample service parameters in the sample data into an initial model to obtain a sample prediction result corresponding to each training sample;
determining a training loss value according to the sample prediction result and the first reference result;
repeating training on the initial model based on the training loss value until the preset predictor meeting the training ending condition is obtained;
the training module is specifically used for:
acquiring initial service parameters of at least one sample service;
screening the initial service parameters, and determining the sample service parameters of which the weight values meet preset conditions in the initial service parameters;
The training module is specifically used for:
calculating the association degree between at least two initial service parameters;
taking the initial service parameters with the association degree smaller than a first preset value as first screening parameters;
determining the sample service parameters of which the weight values meet prior conditions in the first screening parameters; wherein the prior condition comprises preset condition parameters;
the training module is specifically used for:
calculating the association degree according to the initial service parameters and the first data relationship;
wherein the first data relationship comprises:
；
wherein ,representing the degree of association;
representing said initial traffic parameter->Is the first of (2)i values; />Representation->Average value of (2); />The i-th value of said initial traffic parameter y, is->Represents the average value of y;
the training module is specifically used for:
prior to determining the sample traffic parameters for which the weight values in the first screening parameters meet an a priori condition,
dividing the value range of the first screening parameter into at least two numerical intervals;
determining the weight value according to the number of positive samples in the interval, the number of negative samples in the interval, the total number of positive samples, the total number of negative samples and the second data relationship corresponding to each numerical interval;
The second data relationship comprises:
wherein ,representing the weight value;
representing the number of positive samples in the interval; />Representing the total number of positive samples; />Representing the number of negative samples of the interval;representing the total number of negative samples; />Representing the number of said numerical intervals; />Representation pair->Taking the logarithm.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the communication quality of service evaluation method according to any one of claims 1-4.
7. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the communication quality of service evaluation method of any one of claims 1-4.
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