CN112383829B - Experience quality evaluation method and device - Google Patents
Experience quality evaluation method and device Download PDFInfo
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- CN112383829B CN112383829B CN202010087172.2A CN202010087172A CN112383829B CN 112383829 B CN112383829 B CN 112383829B CN 202010087172 A CN202010087172 A CN 202010087172A CN 112383829 B CN112383829 B CN 112383829B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/475—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data
- H04N21/4756—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data for rating content, e.g. scoring a recommended movie
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44218—Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
Abstract
The embodiment of the invention provides an experience quality evaluation method and device, relates to the technical field of communication, and aims to objectively evaluate experience quality in a manner of covering client factors. The method comprises the following steps: acquiring at least one key index of a monitored system and a subjective score corresponding to each key index according to business requirements; performing quality evaluation on the at least one key index through a quality evaluation model to obtain a model score of each key index; determining whether a key index with wrong subjective score exists, wherein the key index with wrong subjective score is a key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value; if so, correcting the subjective score corresponding to the key index with wrong subjective score through an electroencephalogram scoring model; and inputting the key indexes with wrong subjective scores into the quality evaluation model again for quality evaluation. The embodiment of the invention is used for evaluating the experience quality.
Description
The invention discloses an invention name 'an experience quality evaluation method and device' submitted in 11/06/2019, and application numbers are as follows: 201911074024.0 priority of the previous application.
Technical Field
The invention relates to the technical field of communication, in particular to a method and a device for evaluating experience quality.
Background
With the rapid development of wireless communication technology, 2G and 3G networks are gradually eliminated, 4G networks are highly mature, 5G networks are rapidly developed and built, and wireless networks gradually transition from being centered on network equipment to being centered on terminal clients. The operator's evaluation of the network quality will also be made by the requirements of the end customer. This has resulted in the concept of Quality of Experience (QOE). The method refers to the subjective experience of a terminal user on a service and a network, is a comprehensive psychological experience established by the terminal user in the service using process, and relates to all aspects in the interaction process of people, the network, the service and the like. The experience quality can reflect the relation between the quality of the current service and network and the user experience, integrates all the influencing factors of a service level, a user level and a network level, and directly reflects the approval degree of a terminal user to the network service.
The method for evaluating the quality of experience by depending on the terminal client is a typical QOE evaluation method and belongs to an explicit response mode. The evaluation method fully embodies the comprehensive experience of the user on the service quality. However, the evaluation method has the disadvantages that the number of terminal client groups is large, the scoring dynamics is influenced by human body prejudice, the quality evaluation results with highly consistent rules are difficult to obtain, and the upper-layer application analysis based on the QOE results is inconvenient. In order to solve the above problem of the subjective scoring method, there are some objective evaluation methods, such as embedding a decoding function into a testing device and performing quality scoring by evaluating a key index of a video. This is an objective video quality analysis method. By adopting the scheme, the consistency of the quality evaluation result can be ensured. However, the biggest defect of the objective evaluation method is that the factors of the client, such as age, sex, natural environment and the like, cannot be covered. Therefore, how to objectively and cover the customer factors and evaluate the experience quality is a technical problem to be solved urgently.
Disclosure of Invention
In view of this, the invention provides an experience quality evaluation method and device, which are used for objectively evaluating experience quality in a manner of covering client factors.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides a method for evaluating quality of experience, including:
acquiring at least one key index of a monitored system and a subjective score corresponding to each key index according to service requirements;
performing quality evaluation on the at least one key index through a quality evaluation model to obtain a model score of each key index; the quality evaluation model is a model obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples;
determining whether a key index with wrong subjective score exists, wherein the key index with wrong subjective score is a key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value;
if yes, correcting the subjective score corresponding to the key index with wrong subjective score through an electroencephalogram assignment model, wherein the electroencephalogram assignment model comprises the corresponding relation between each key index and an electroencephalogram signal, and the electroencephalogram signal corresponding to any key index is used for indicating the subjective score of the key index;
and inputting the key indexes with wrong subjective scores into the quality evaluation model again for quality evaluation.
As an optional implementation manner of the embodiment of the present invention, before the correcting the subjective score of the key index with the wrong subjective score by using the electroencephalogram scoring model, the method further includes:
acquiring an electroencephalogram test excitation source corresponding to each key index in the at least one key index;
acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source;
analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source;
and establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective scoring of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source includes:
respectively outputting each electroencephalogram test excitation source;
and acquiring the electroencephalogram signals of the tested individuals to acquire the electroencephalogram signals corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, before analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source, the method further includes:
carrying out power normalization processing on the electroencephalogram signals;
filtering useless frequency components in the electroencephalogram signals after the power normalization processing;
and eliminating abnormal data points in the electroencephalogram signals after filtering out useless frequency components.
As an optional implementation manner in the embodiment of the present invention, the acquiring at least one key index of the monitored system and a subjective score corresponding to each key index according to the service requirement includes:
periodically or in a time-triggered manner, collecting each key index of the monitored system as a data field respectively, and recording the subjective score corresponding to each key index;
and extracting the at least one key index and the subjective score corresponding to each key index from the acquired key indexes in a data field screening mode according to the service requirement.
In a second aspect, an embodiment of the present invention provides an apparatus for evaluating quality of experience, including:
the acquisition module is used for acquiring at least one key index of the monitored system and subjective scores corresponding to each key index according to the service requirements;
the evaluation module is used for evaluating the quality of the at least one key index through the quality evaluation model to obtain the model score of each key index; the quality evaluation model is a model obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples;
the processing module is used for determining whether a key index with wrong subjective score exists, wherein the key index with wrong subjective score is a key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value;
the correction module is used for correcting the subjective score corresponding to the key index with the wrong subjective score through an electroencephalogram scoring model under the condition that the key index with the wrong subjective score exists, the electroencephalogram scoring model comprises the corresponding relation between each key index and an electroencephalogram signal, and the electroencephalogram signal corresponding to any key index is used for indicating the subjective score of the key index;
and the evaluation module is also used for inputting the key indexes with wrong subjective scores into the quality evaluation model again for quality evaluation.
As an optional implementation manner of the embodiment of the present invention, the experience quality evaluation device further includes:
the establishing module is used for acquiring an electroencephalogram test excitation source corresponding to each key index in the at least one key index; acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source; analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source; and establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective scoring of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the establishing module is specifically configured to output each of the electroencephalogram test excitation sources respectively; and acquiring electroencephalogram signals of the tested individual to acquire the electroencephalogram signals corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the establishing module is further configured to perform power normalization processing on the electroencephalogram signals before analyzing the electroencephalogram signals to determine a subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source; filtering useless frequency components in the electroencephalogram signals after the power normalization processing; and eliminating abnormal data points in the electroencephalogram signals after filtering out useless frequency components.
As an optional implementation manner of the embodiment of the present invention, the obtaining module is specifically configured to collect, periodically or in a time-triggered manner, each key index of the monitored system as a data field, record a subjective score corresponding to each key index, and extract, according to a service requirement, the at least one key index and the subjective score corresponding to each key index from the collected key indexes in a data field screening manner.
In a third aspect, an embodiment of the present invention provides an experience quality assessment apparatus, including: a memory for storing a computer program and a processor; the processor is configured to execute the method for quality of experience assessment according to the first aspect or any embodiment of the first aspect when the computer program is invoked.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for evaluating quality of experience according to the first aspect or any of the embodiments of the first aspect.
According to the experience quality evaluation method provided by the embodiment of the invention, after at least one key index of a monitored system and the subjective score corresponding to each key index are obtained according to business requirements, firstly, quality evaluation is carried out on the at least one key index respectively through a quality evaluation model, the model score of each key index is obtained, then whether the key index with wrong subjective score exists is determined according to the absolute value of the difference value between the model score of the key index and the subjective score, if the key index with wrong subjective score exists, the subjective score corresponding to the key index with wrong subjective score is corrected through an electroencephalogram assignment model, and then the key index with wrong subjective score is input into the quality evaluation model again for quality evaluation; because the electroencephalogram assignment model in the embodiment of the invention in China is purchased by running and comprises the corresponding relation between each key index and the electroencephalogram signal, the human perception system can automatically and immediately react to the characteristic change of the environment, meanwhile, the human perception system belongs to a biological characteristic and has reaction objectivity, the brain is a control center of various feelings and emotions of human beings, the change of the electroencephalogram signal can cover the reaction of the human beings to an excitation source and the natural environment, and the influence factors of the biological characteristics of the human beings such as age, sex and the like are also superposed, so that the comprehensive experience of the human beings can be accurately reflected, the subjective score corresponding to the key index with wrong subjective score can be corrected through the electroencephalogram assignment model, the influence of user factors on the subjective score can be eliminated, abnormal data in the subjective quality score can be monitored and corrected, so that the embodiment of the invention can objectively and cover the evaluation of the user factors on the experience quality, the accuracy of the subjective quality evaluation result is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings.
Fig. 1 is a flowchart illustrating steps of a method for evaluating quality of experience according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system for performing a method for quality of experience assessment according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating an operation principle of a tag calibration module according to an embodiment of the present invention;
FIG. 4 is a second flowchart illustrating the steps of a method for evaluating quality of experience according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a system for acquiring electroencephalogram signals according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an electroencephalogram acquisition device provided by an embodiment of the present invention;
FIG. 7 is a flowchart illustrating the steps of processing an electroencephalogram signal according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an experience quality evaluation apparatus according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a quality of experience evaluation system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second," and the like, in the description and in the claims of the present invention are used for distinguishing between synchronized objects, and are not used to describe a particular order of objects. For example, the first interface and the second interface, etc. are for distinguishing different interfaces, rather than for describing a particular order of the interfaces.
In the embodiments of the present invention, words such as "exemplary" or "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion. Further, in the description of the embodiments of the present invention, "a plurality" means two or more unless otherwise specified.
The embodiment of the present invention provides a method for evaluating quality of experience, and specifically, with reference to a flowchart of the method for evaluating quality of experience shown in fig. 1 and a structure of a main body for executing the method for evaluating quality of experience shown in fig. 2, the method for evaluating quality of experience provided by the embodiment of the present invention includes the following steps S11 to S15:
and S11, acquiring at least one key index of the monitored system and the subjective score corresponding to each key index according to the service requirement.
As an optional implementation manner of the embodiment of the present invention, the obtaining, in the step S11, at least one key index of the monitored system and a subjective score corresponding to each key index according to the service requirement may include the following steps a and b:
and a, periodically or in a time-triggered manner, collecting each key index of the monitored system as a data field, and recording the subjective score corresponding to each key index.
And b, extracting the at least one key index and the subjective score corresponding to each key index from the acquired key indexes in a data field screening mode according to the service requirement.
Illustratively, the key indicators in the embodiment of the present invention may include: the method comprises the following steps of time delay, pause frequency, average pause time, packet loss rate, reference signal quality, signal strength, block error rate and the like.
Specifically, the key indexes are from the monitored system, and the subjective scores corresponding to the key indexes are from the body to be subjected to quality evaluation. The subjective evaluation data can be acquired through the key indexes and the subjective evaluation data acquisition interfaces corresponding to the key indexes. The key indexes and the subjective scoring data acquisition interfaces corresponding to the key indexes are defined as follows:
a data acquisition carrier: the data acquisition carrier exists in a hardware or software mode;
the data acquisition mode is as follows: the method is carried out periodically or in an event triggering mode, key indexes of a monitored system are used as different data fields for collection, and meanwhile, subjective scores corresponding to each group of collected samples are recorded, wherein the subjective scores can be single-dimensional or multi-dimensional.
A database: the system is used for storing each key index of the monitored system and the subjective score corresponding to each key index. The database may be located in a server, cloud, or other physical storage. When the service requirements exist, the required key indexes and the corresponding subjective scores can be filtered through the field screening function according to different service requirements.
The above step S11 may be performed by the data acquisition system 21 as shown in fig. 2. The data acquisition system can be composed of a data acquisition interface and a database.
And S12, respectively carrying out quality evaluation on the at least one key index through a quality evaluation model, and obtaining the model score of each key index.
The quality evaluation model is obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples.
Since the quality evaluation of the key index needs to be performed by the quality evaluation model in step S12, before step S12, the quality evaluation model needs to be constructed first. The construction of the quality evaluation model mainly comprises two parts of main contents:
one part is as follows: and (6) acquiring training data. The training data in the embodiment of the invention can be acquired from big data and acquired by acquisition equipment. The embodiment of the invention does not limit the acquisition mode of the training sample data (key indexes and subjective scores corresponding to the key indexes) of the quality evaluation model, and the acquisition of the key indexes and the subjective scores corresponding to the key indexes is taken as the standard.
The other part is as follows: and (4) training a model. The quality evaluation model depends on a machine learning algorithm or a neural network algorithm and the like. And different algorithms are selected according to different service requirements (such as fault analysis, system simulation, network optimization and the like). The specific algorithms and functions of the algorithm model library are not within the scope of the embodiments of the present invention, and will not be described in detail here.
The above step S12 may be performed by the quality evaluation system 24 shown in fig. 2.
And S13, determining whether the key indexes with wrong subjective scores exist.
And the key index with wrong subjective score is the key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value.
The threshold value in the embodiment of the invention can be determined by a person skilled in the art according to the value range of subjective score and model score. For example: if the value range of the subjective score and the model score is [0,100], the threshold value can be set to 10, that is, if the absolute value of the difference between the subjective score and the model score of any key index is greater than 10, the key index is determined to be the key index with wrong subjective score. For another example: if the value range of the subjective score and the model score is [0,100], the threshold value can be set to 20, that is, if the absolute value of the difference between the subjective score and the model score of any key index is greater than 20, the key index is determined to be the key index with wrong subjective score. For another example: if the value ranges of the subjective score and the model score are [0,5], the threshold value can be set to be 0, that is, if the subjective score and the model score of any key index are different, the key index is determined to be the key index with wrong subjective score.
The above step S13 may be performed by the quality evaluation system 24 as shown in fig. 2.
If it is determined in the above step S13 that there is a key index having an erroneous subjective score, the following step S14 is executed.
And S14, correcting the subjective score corresponding to the key index with wrong subjective score through an electroencephalogram scoring model.
The Electroencephalogram scoring model comprises corresponding relations between each key index and an Electroencephalogram signal, and the Electroencephalogram signal (EEG) corresponding to any key index is used for indicating subjective scoring of the key index.
For example: and correcting the subjective score of the key index A to X if the electroencephalogram signal corresponding to the key index A in the electroencephalogram scoring model is an electroencephalogram signal B and the subjective score indicated by the electroencephalogram signal is X.
Specifically, the electroencephalogram scoring model may be a model obtained by training in a machine learning method, such as, but not limited to, a KNN algorithm.
Exemplarily, referring to fig. 3, the process of correcting the subjective score corresponding to the key index having the incorrect subjective score through the electroencephalogram scoring model includes: the data index extraction module 31 extracts the key indexes with wrong subjective scores and sends the extracted key indexes to the label correction module 32. The label correction module 32 obtains and outputs the corrected subjective score according to the key index and the electroencephalogram scoring model. Finally, the new label packing module 33 adds the corrected subjective scores to the key indexes with wrong subjective scores, and inputs the key indexes to the quality evaluation model again for quality evaluation again. For example, when performing the corrected tag mapping, the index set D may be a multidimensional array composed of input fields, for example, D1 ═ D11, D12, D13, …, D1N.
The above step S14 can be executed by the electroencephalogram processing system 22 and the subjective score correction system 23 as shown in fig. 2.
And S15, inputting the key indexes with wrong subjective scores into the quality evaluation model again for quality evaluation.
The method comprises the steps of acquiring a subjective score, obtaining a corrected subjective score according to an electroencephalogram scoring system, packaging the corrected subjective score and the corresponding key index, sending the packaged subjective score and the corresponding key index back to a quality evaluation model for model quality evaluation again, and outputting an evaluation result until the key index with the error in the subjective score does not exist.
Also, the above step S15 can be performed by the quality evaluation system 24 as shown in FIG. 2.
According to the experience quality evaluation method provided by the embodiment of the invention, after at least one key index of a monitored system and the subjective score corresponding to each key index are obtained according to business requirements, firstly, quality evaluation is carried out on the at least one key index respectively through a quality evaluation model, the model score of each key index is obtained, then whether the key index with wrong subjective score exists is determined according to the absolute value of the difference value between the model score of the key index and the subjective score, if the key index with wrong subjective score exists, the subjective score corresponding to the key index with wrong subjective score is corrected through an electroencephalogram assignment model, and then the key index with wrong subjective score is input into the quality evaluation model again for quality evaluation; because the electroencephalogram assignment model in the embodiment of the invention in China is purchased by running and comprises the corresponding relation between each key index and the electroencephalogram signal, the human perception system can automatically and immediately react to the characteristic change of the environment, meanwhile, the human perception system belongs to a biological characteristic and has reaction objectivity, the brain is a control center of various feelings and emotions of human beings, the change of the electroencephalogram signal can cover the reaction of the human beings to an excitation source and the natural environment, and the influence factors of the biological characteristics of the human beings such as age, sex and the like are also superposed, so that the comprehensive experience of the human beings can be accurately reflected, the subjective score corresponding to the key index with wrong subjective score can be corrected through the electroencephalogram assignment model, the influence of user factors on the subjective score can be eliminated, abnormal data in the subjective quality score can be monitored and corrected, so that the embodiment of the invention can objectively and cover the evaluation of the user factors on the experience quality, the accuracy of the subjective quality evaluation result is improved.
Further, as shown in fig. 4, an electroencephalogram assignment model is required in the experience quality evaluation process in the foregoing embodiment, so before the step S14 (correcting the subjective score corresponding to the key index with the incorrect subjective score through the electroencephalogram assignment model), the method provided in the embodiment of the present invention further requires to construct an electroencephalogram assignment model. As an optional implementation manner of the embodiment of the present invention, the process of constructing the electrical assignment model may include the following steps:
and S41, acquiring the electroencephalogram test excitation source corresponding to each key index in the at least one key index.
After the service to be detected is determined, the field data of the required key indexes are extracted, and the corresponding electroencephalogram test excitation source is designed according to the key indexes.
And S42, acquiring the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the step S42 (obtaining the electroencephalogram signal corresponding to each of the electroencephalogram test excitation sources) includes the following steps 1 and 2.
And step 1, outputting each electroencephalogram test excitation source respectively.
And 2, acquiring electroencephalogram signals of the tested individuals to obtain the electroencephalogram signals corresponding to the electroencephalogram test excitation sources.
The electroencephalogram test method comprises the steps of carrying out an electroencephalogram test based on electroencephalogram test excitation sources, and obtaining an electroencephalogram signal corresponding to each electroencephalogram test excitation source in an experimental mode.
For example, an implementation system for performing an electroencephalogram experiment and acquiring an electroencephalogram signal can be shown in fig. 5, and includes: the device comprises an excitation source output device 51, an electroencephalogram signal acquisition terminal 52, an electroencephalogram signal acquisition device 53, an electroencephalogram signal storage device 54 and a tested individual 55.
The excitation source output device 51 is used for outputting electroencephalogram test excitation to a tested individual 55, the electroencephalogram signal acquisition terminal 52 and the electroencephalogram signal acquisition equipment 53 are used for acquiring electroencephalogram signals, and the electroencephalogram signal storage equipment 54 is used for storing the acquired electroencephalogram signals.
Because the brain has different reflection areas for different stimuli and the electroencephalogram signals have complex propagation paths in the cranial cavity, the electroencephalogram signal acquisition terminal 52 can adopt 64T (64 acquisition sensors) non-contact acquisition equipment in order to acquire accurate and comprehensive electroencephalogram signals. The structure of the 64T non-contact acquisition device is shown in fig. 6.
It should be noted that the 64T electroencephalogram signal acquisition terminal 52 is only an example, the electroencephalogram signal acquisition terminal 52 may also be other types of devices, such as 32T, 128T, and the like, and the electroencephalogram signal acquisition terminal 52 may economically affect the quality of the acquired electroencephalogram signal, and may not affect the essence of the method mentioned in the present invention.
And S43, analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
Specifically, an electroencephalogram signal is a method for reflecting human brain activity using physiological indexes, which is formed by the sum of postsynaptic potentials generated by a large number of neurons simultaneously when the brain is active. It records the change of electric wave during brain activity, and is the overall reflection of the physiological activity of brain nerve cells on the surface of cerebral cortex or scalp. Because the reaction areas of the human brain to different external stimuli are different and the brain waves of the reactions are also different, human perception changes can be carried out on the basis of changes of the brain electrical signals, and therefore corresponding subjective scores can be determined by analyzing the brain electrical signals.
The implementation mode of analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signals corresponding to each electroencephalogram test excitation source can be as follows: the algorithm extracts the signal change characteristics (such as wavelet change, curvelet transform and the like) of the electroencephalogram signal, induces the extracted signal change characteristics through a clustering algorithm, and matches the extracted signal change characteristics with an input excitation source used in an experiment.
In addition, the electroencephalogram signal has the advantage of quick response, so that the electroencephalogram signal is suitable for sensing acquisition triggered by quick environmental change, such as video, audio and other services.
S44, establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
Because the electroencephalogram assignment model is established based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source, the electroencephalogram signal corresponding to the electroencephalogram test excitation source and the subjective score of the electroencephalogram signal corresponding to the key index can be obtained by inputting one key index into the electroencephalogram assignment model. That is, the subjective score corresponding to the key index may be obtained.
It should be noted that the electroencephalogram scoring model may be a multidimensional mapping table or a multidimensional clustering algorithm model. The algorithms mentioned herein are only examples of response signal processing and are not limited to these algorithms.
Referring to fig. 7, before step S43 (analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source), the method provided in the embodiment of the present invention further includes:
and S71, preprocessing the electroencephalogram signals. Specifically, the preprocessing of the electroencephalogram signal may include the following steps:
and S711, carrying out power normalization processing on the electroencephalogram signals.
And S712, filtering useless frequency components in the electroencephalogram signals after the power normalization processing.
Specifically, the electroencephalogram signal after the power normalization processing can be output to a band-pass filter, so that useless frequency components in the electroencephalogram signal are filtered.
S713, removing abnormal data points in the electroencephalogram signals after useless frequency components are filtered.
When being tested, the tested individual may be stimulated by other stimulation sources (factors such as jitter, blinking, external environment stimulation and the like) except the electroencephalogram test stimulation source, and the stimulation of the stimulation sources can cause fluctuation of the electroencephalogram signals, so that the accuracy of the electroencephalogram signals is influenced. According to the embodiment, the abnormal data points in the electroencephalogram signals are removed and filtered out before the electroencephalogram signals are analyzed, so that the accuracy of the constructed electroencephalogram scoring model can be improved.
Based on the same inventive concept, as an implementation of the foregoing method, an embodiment of the present invention further provides an experience quality assessment apparatus, where the apparatus embodiment corresponds to the foregoing method embodiment, and for convenience of reading, details in the foregoing method embodiment are not repeated one by one in the apparatus embodiment, but it should be clear that the apparatus in the embodiment can correspondingly implement all the contents in the foregoing method embodiment.
Fig. 8 is a schematic structural diagram of an experience quality evaluation device according to an embodiment of the present invention, and as shown in fig. 8, an experience quality evaluation device 800 according to the embodiment includes:
the acquiring module 81 is configured to acquire at least one key index of the monitored system and a subjective score corresponding to each key index according to a service requirement;
the evaluating module 82 is used for evaluating the quality of the at least one key index through a quality evaluating model to obtain a model score of each key index; the quality evaluation model is a model obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples;
the processing module 83 is configured to determine whether a key index with wrong subjective score exists, where the key index with wrong subjective score is a key index with an absolute value of a difference between the model score and the subjective score larger than a threshold;
the correction module 84 is configured to correct the subjective score corresponding to the key index with the wrong subjective score through an electroencephalogram scoring model under the condition that the key index with the wrong subjective score exists, where the electroencephalogram scoring model includes a correspondence between each key index and an electroencephalogram signal, and the electroencephalogram signal corresponding to any key index is used for indicating the subjective score of the key index;
the evaluation module 82 is further configured to input the key index with the wrong subjective score into the quality evaluation model again for quality evaluation.
As an optional implementation manner of the embodiment of the present invention, referring to fig. 8, the experience quality assessment apparatus 800 further includes:
the establishing module 85 is used for extracting at least one key index according to the service requirement; acquiring an electroencephalogram test excitation source corresponding to each key index in the at least one key index; acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source; analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source; and establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the establishing module 85 is specifically configured to output each of the electroencephalogram test excitation sources respectively; and acquiring electroencephalogram signals of the tested individual to acquire the electroencephalogram signals corresponding to each electroencephalogram test excitation source.
As an optional implementation manner of the embodiment of the present invention, the establishing module 85 is further configured to perform power normalization processing on the electroencephalogram signal before analyzing the electroencephalogram signal to determine a subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source; filtering useless frequency components in the electroencephalogram signals after the power normalization processing; and eliminating abnormal data points in the electroencephalogram signals after filtering out useless frequency components.
As an optional implementation manner of the embodiment of the present invention, the obtaining module 81 is specifically configured to collect, periodically or in a time-triggered manner, each key index of the monitored system as a data field, record a subjective score corresponding to each key index, and extract, according to a service requirement, the at least one key index and the subjective score corresponding to each key index from the collected key indexes in a data field screening manner.
The experience quality evaluation device provided by this embodiment may execute the experience quality evaluation method provided by the above method embodiment, and the implementation principle and the technical effect thereof are similar, and are not described herein again.
Based on the same inventive concept, the embodiment of the invention also provides experience quality evaluation equipment. Fig. 9 is a schematic structural diagram of the experience quality evaluation device provided in the embodiment of the present invention, and as shown in fig. 9, the experience quality evaluation device provided in this embodiment includes: a memory 91 and a processor 92, the memory 91 being for storing a computer program; the processor 92 is configured to execute the steps of the method for evaluating quality of experience according to the above-mentioned method embodiment when the computer program is called.
The methods described herein may be implemented in various forms of software, hardware, or a combination of software and hardware. Fig. 10 below is an example of an embodiment based on a combination of hardware and software.
The acquisition terminal can exist in a hardware/SDK/APP mode, data acquisition is completed, the data are transmitted to a database of the cloud, the cloud comprises all database storage, electroencephalogram processing, model processing, data correction and quality evaluation-based expansion application, and all cloud results and function control are achieved through a user UI and a user interface. The user UI may exist in the form of hardware/SDK/APP.
The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes the experience quality evaluation method in the embodiment of the method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the media.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer readable media include both permanent and non-permanent, removable and non-removable storage media. Storage media may implement information storage by any method or technology, and the information may be computer-readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (12)
1. A quality of experience assessment method, comprising:
acquiring at least one key index of a monitored system and a subjective score corresponding to each key index according to business requirements;
performing quality evaluation on the at least one key index through a quality evaluation model to obtain a model score of each key index; the quality evaluation model is a model obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples;
determining whether a key index with wrong subjective score exists, wherein the key index with wrong subjective score is a key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value;
if yes, correcting the subjective score corresponding to the key index with wrong subjective score through an electroencephalogram assignment model, wherein the electroencephalogram assignment model comprises the corresponding relation between each key index and an electroencephalogram signal, and the electroencephalogram signal corresponding to any key index is used for indicating the subjective score of the key index;
and inputting the key indexes with wrong subjective scores and the corresponding corrected subjective scores into the quality evaluation model again for quality evaluation.
2. The method of claim 1, wherein before correcting the subjective score of the key indicator that the subjective score is incorrect by the electroencephalogram scoring model, the method further comprises:
acquiring an electroencephalogram test excitation source corresponding to each key index in the at least one key index;
acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source;
analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source;
and establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective scoring of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
3. The method of claim 2, wherein said obtaining the brain electrical signal corresponding to each of said brain electrical test stimuli comprises:
respectively outputting each electroencephalogram test excitation source;
and acquiring electroencephalogram signals of the tested individual to acquire the electroencephalogram signals corresponding to each electroencephalogram test excitation source.
4. The method of claim 2, wherein prior to analyzing the brain electrical signals to determine a subjective score of the brain electrical signals corresponding to each of the brain electrical test stimuli, the method further comprises:
carrying out power normalization processing on the electroencephalogram signals;
filtering useless frequency components in the electroencephalogram signals after the power normalization processing;
and eliminating abnormal data points in the electroencephalogram signals after useless frequency components are filtered out.
5. The method according to any one of claims 1 to 4, wherein the obtaining of at least one key index of the monitored system and a subjective score corresponding to each key index according to the service requirement comprises:
periodically or in a time-triggered manner, collecting each key index of the monitored system as a data field respectively, and recording the subjective score corresponding to each key index;
and extracting the at least one key index and the subjective score corresponding to each key index from the acquired key indexes in a data field screening mode according to the service requirement.
6. An experience quality evaluation device, comprising:
the acquisition module is used for acquiring at least one key index of the monitored system and subjective scores corresponding to each key index according to the service requirements;
the evaluation module is used for evaluating the quality of the at least one key index through the quality evaluation model to obtain the model score of each key index; the quality evaluation model is a model obtained by performing model training by taking the key indexes and subjective scores corresponding to the key indexes as training samples;
the processing module is used for determining whether a key index with wrong subjective score exists, wherein the key index with wrong subjective score is a key index of which the absolute value of the difference value between the model score and the subjective score is greater than a threshold value;
the correction module is used for correcting the subjective score corresponding to the key index with the wrong subjective score through an electroencephalogram scoring model under the condition that the key index with the wrong subjective score exists, the electroencephalogram scoring model comprises the corresponding relation between each key index and an electroencephalogram signal, and the electroencephalogram signal corresponding to any key index is used for indicating the subjective score of the key index;
and the evaluation module is also used for inputting the key indexes with wrong subjective scores and the corresponding corrected subjective scores into the quality evaluation model again for quality evaluation.
7. The apparatus of claim 6, wherein the quality of experience assessment apparatus further comprises:
the establishing module is used for acquiring an electroencephalogram test excitation source corresponding to each key index in the at least one key index; acquiring an electroencephalogram signal corresponding to each electroencephalogram test excitation source; analyzing the electroencephalogram signals to determine the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source; and establishing the electroencephalogram scoring model based on the corresponding relation between each key index in the at least one key index and the electroencephalogram test excitation source and the subjective score of the electroencephalogram signal corresponding to each electroencephalogram test excitation source.
8. The device according to claim 7, wherein said building module is specifically configured to output each of said brain electrical test stimulus sources separately; and acquiring electroencephalogram signals of the tested individual to acquire the electroencephalogram signals corresponding to each electroencephalogram test excitation source.
9. The device of claim 7, wherein the establishing module is further configured to perform power normalization on the electroencephalogram signal before analyzing the electroencephalogram signal to determine a subjective score of the electroencephalogram signal corresponding to each of the electroencephalogram test stimulus sources; filtering useless frequency components in the electroencephalogram signals after the power normalization processing; and eliminating abnormal data points in the electroencephalogram signals after filtering out useless frequency components.
10. The apparatus according to any one of claims 6 to 9, wherein the obtaining module is specifically configured to collect, periodically or in a time-triggered manner, each key index of the monitored system as a data field, record a subjective score corresponding to each key index, and extract, according to a service requirement, the at least one key index and the subjective score corresponding to each key index from the collected key indexes in a data field screening manner.
11. A quality of experience assessment device comprising a memory for storing a computer program and a processor; the processor is adapted to execute the method of quality of experience assessment according to any of claims 1-5 when the computer program is invoked.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of quality of experience assessment according to any one of claims 1-5.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009110817A1 (en) * | 2008-02-29 | 2009-09-11 | Mukhin Aleksandr Vladimirovich | Method for computer-aided modeling of a person semantic sphere, a method for forming pictures of textual images and a system for carrying out said method |
| CN104814734A (en) * | 2015-05-13 | 2015-08-05 | 西安电子科技大学 | CSP (Common Spatial Patterns) and cross-correlation based motor imagery electroencephalogram classification method |
| CN105721863A (en) * | 2016-02-02 | 2016-06-29 | 上海师范大学 | Video quality evaluation method |
| CN107609492A (en) * | 2017-08-25 | 2018-01-19 | 西安电子科技大学 | Distorted image quality based on EEG signals perceives evaluation method |
| CN109428759A (en) * | 2017-09-01 | 2019-03-05 | 中国移动通信集团广西有限公司 | A kind of network quality appraisal procedure and device |
| CN109476310A (en) * | 2016-12-30 | 2019-03-15 | 同济大学 | A kind of automatic Pilot method for controlling driving speed based on comfort level |
| CN110279415A (en) * | 2019-07-01 | 2019-09-27 | 西安电子科技大学 | Image fault threshold coefficient estimation method based on EEG signals |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104640125B (en) * | 2013-11-08 | 2018-01-30 | 中国移动通信集团浙江有限公司 | A kind of method and apparatus of the quality of assessment WLAN business |
| CN105046708B (en) * | 2015-07-14 | 2017-11-17 | 福州大学 | A kind of color correction objective evaluation method consistent with subjective perception |
| US11006834B2 (en) * | 2016-11-08 | 2021-05-18 | Sony Corporation | Information processing device and information processing method |
| EP3321831B1 (en) * | 2016-11-14 | 2019-06-26 | Carl Zeiss Vision International GmbH | Device for determining predicted subjective refraction data or predicted subjective correction data and computer program |
| CN106840230A (en) * | 2017-02-24 | 2017-06-13 | 河北稳控科技有限公司 | The signal processing apparatus and method of a kind of vibrating string type sensor |
| CN108491955A (en) * | 2018-02-07 | 2018-09-04 | 北京理工大学 | A kind of monthly industrial power predicating method of integrated index screening and temperature correction |
| CN110008874B (en) * | 2019-03-25 | 2021-05-18 | 联想(北京)有限公司 | Data processing method and device, computer system and readable medium |
-
2020
- 2020-02-11 CN CN202010087172.2A patent/CN112383829B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009110817A1 (en) * | 2008-02-29 | 2009-09-11 | Mukhin Aleksandr Vladimirovich | Method for computer-aided modeling of a person semantic sphere, a method for forming pictures of textual images and a system for carrying out said method |
| CN104814734A (en) * | 2015-05-13 | 2015-08-05 | 西安电子科技大学 | CSP (Common Spatial Patterns) and cross-correlation based motor imagery electroencephalogram classification method |
| CN105721863A (en) * | 2016-02-02 | 2016-06-29 | 上海师范大学 | Video quality evaluation method |
| CN109476310A (en) * | 2016-12-30 | 2019-03-15 | 同济大学 | A kind of automatic Pilot method for controlling driving speed based on comfort level |
| CN107609492A (en) * | 2017-08-25 | 2018-01-19 | 西安电子科技大学 | Distorted image quality based on EEG signals perceives evaluation method |
| CN109428759A (en) * | 2017-09-01 | 2019-03-05 | 中国移动通信集团广西有限公司 | A kind of network quality appraisal procedure and device |
| CN110279415A (en) * | 2019-07-01 | 2019-09-27 | 西安电子科技大学 | Image fault threshold coefficient estimation method based on EEG signals |
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