Disclosure of Invention
In order to improve the noise reduction effect of the noise reduction earphone when the noise reduction earphone is used for a user, the application provides a test method and a test system of the noise reduction earphone.
The first object of the present invention is achieved by the following technical solutions:
a test method of a noise reduction earphone comprises the following steps:
acquiring a scene test type and a noise reduction algorithm model, and acquiring corresponding noise simulation data according to the scene test type;
acquiring audio output reference data, inputting the noise simulation data into the noise reduction algorithm model one by one according to the scene test type for testing, and comparing the acquired audio noise reduction result with the audio output reference data respectively to obtain a noise reduction test result;
correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type;
and generating an earphone noise reduction model according to each noise reduction algorithm, and generating a model test message according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
Through adopting above-mentioned technical scheme, when carrying out the test to the noise reduction algorithm in the earphone of making an uproar, through obtaining corresponding noise simulation data according to scene test type, and then can simulate the noise of different scenes, carry out the test of making an uproar, thereby can make an uproar to the noise scene of different scenes pertinently, simultaneously, when carrying out the test, the result of test, noise reduction algorithm generates the earphone model of making an uproar that corresponds promptly, thereby can promote the suitability of noise reduction algorithm and every earphone, and can promote the richness of noise simulation data through the mode of constantly enriching scene test type, and then help promoting the user when making an uproar of making an uproar in follow-up use of earphone, the earphone is to the adaptive degree of environment, with the noise reduction effect of promotion earphone.
The present application may be further configured in a preferred example to: the step of obtaining audio output reference data, which is to input the noise simulation data into the noise reduction algorithm model one by one according to the scene test type for testing, and respectively compare the obtained audio noise reduction result with the audio output reference data to obtain a noise reduction test result, specifically comprising the following steps:
extracting noise characteristic data corresponding to each piece of noise simulation data, and respectively inputting each piece of noise characteristic data into the noise algorithm model;
acquiring the audio noise reduction result corresponding to each noise characteristic data from the noise algorithm model, and extracting each audio noise reduction result to extract the audio noise reduction characteristic data;
and extracting audio reference features from the audio output reference data, and respectively comparing the audio reference features with each audio noise reduction feature to obtain the noise reduction test result corresponding to each noise feature data.
By adopting the technical scheme, because the effect requirement of the user after the noise reduction earphone is used for reducing the noise is unified, when the noise reduction algorithm model is tested, each noise characteristic data is respectively input into the noise reduction algorithm model, so that the noise reduction effect of the noise reduction algorithm model on each noise scene can be tested, namely the audio noise reduction result is compared with the set audio output reference data, the difference between the noise reduction result of the noise reduction algorithm model on each noise simulation data and the audio input reference can be tested, and the subsequent corresponding adjustment is facilitated.
The present application may be further configured in a preferred example to: extracting audio reference features from the audio output reference data, and comparing the audio reference features with each audio noise reduction feature to obtain a noise reduction test result corresponding to each noise feature data, wherein the method specifically comprises the following steps of:
constructing a reference feature vector of the audio reference feature, and constructing a noise reduction feature vector of each audio noise reduction feature;
and calculating comparison distance data of each noise reduction feature vector and the reference feature vector, and taking the comparison distance data as the noise reduction test result.
By adopting the technical scheme, the distance comparison of vectors is carried out by constructing the corresponding feature vectors, so that the difference between each audio noise reduction feature and each audio reference feature can be compared, the actual effect of each audio noise reduction result can be conveniently judged, and the subsequent targeted adjustment of the noise reduction algorithm model to each noise scene is facilitated.
The present application may be further configured in a preferred example to: the noise reduction algorithm model is corrected according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type, and the method specifically comprises the following steps:
sorting each scene test type according to a preset rule, and correcting a noise reduction algorithm model with the first sorting to obtain a first correction result;
inputting noise simulation data corresponding to the second scene test type into the first correction result to obtain correction noise data, and performing difference comparison on the correction noise data and audio output reference data;
if the difference comparison result is within the preset threshold value, the first correction result is used as the corresponding noise reduction algorithm, otherwise, the second noise reduction algorithm model is corrected to obtain a second correction result;
starting from the noise simulation data corresponding to the third scene test type, inputting the corresponding noise simulation data into the corrected results after the noise reduction algorithm model is corrected one by one, and comparing the output results with the audio output reference data in a difference mode so as to screen the corresponding corrected results or correct the noise reduction algorithm model.
By adopting the technical scheme, because the related noise reduction algorithm consumes larger power consumption when the noise reduction earphone is used for noise reduction, especially the use time of the Bluetooth earphone is influenced, the next scene is tested by correcting results obtained by correcting the noise reduction algorithm model one by one, and the applicable correcting results are used as the noise reduction algorithm of the scene at the same time, so that the number of the noise reduction algorithms in the model of the whole noise reduction algorithm can be reduced, the noise reduction algorithm in the noise reduction process is simplified, and the consumption of the noise reduction earphone in use can be reduced.
The present application may be further configured in a preferred example to: the generating the earphone noise reduction model according to each noise reduction algorithm specifically comprises the following steps:
packaging all the noise simulation data to obtain a noise scene recognition model;
and correlating each noise reduction algorithm with corresponding noise simulation data in the noise scene recognition model to obtain an earphone noise reduction model.
By adopting the technical scheme, the noise scene recognition model and the noise reduction algorithm can be separated by adopting the mode of packaging the noise simulation data and correlating the noise scene recognition model with the corresponding noise simulation data, so that scene recognition calculation can be carried out through the terminal connected with the noise reduction earphone through the noise scene recognition model, the calculation of the noise reduction earphone in the noise reduction aspect is further reduced, and the use power consumption of the noise reduction earphone is further reduced.
The second object of the present invention is achieved by the following technical solutions:
a test system for a noise reducing earphone, the test system comprising:
the scene simulation module is used for acquiring a scene test type and a noise reduction algorithm model and acquiring corresponding noise simulation data according to the scene test type;
the algorithm test module is used for acquiring audio output reference data, inputting the noise simulation data into the noise reduction algorithm model one by one according to the scene test type for testing, and comparing the acquired audio noise reduction result with the audio output reference data respectively to obtain a noise reduction test result;
the algorithm correction module is used for respectively correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type;
and the model test module is used for generating an earphone noise reduction model according to each noise reduction algorithm, and generating a model test message according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
Through adopting above-mentioned technical scheme, when carrying out the test to the noise reduction algorithm in the earphone of making an uproar, through obtaining corresponding noise simulation data according to scene test type, and then can simulate the noise of different scenes, carry out the test of making an uproar, thereby can make an uproar to the noise scene of different scenes pertinently, simultaneously, when carrying out the test, the result of test, noise reduction algorithm generates the earphone model of making an uproar that corresponds promptly, thereby can promote the suitability of noise reduction algorithm and every earphone, and can promote the richness of noise simulation data through the mode of constantly enriching scene test type, and then help promoting the user when making an uproar of making an uproar in follow-up use of earphone, the earphone is to the adaptive degree of environment, with the noise reduction effect of promotion earphone.
The third object of the present application is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for testing a noise reducing earpiece described above when the computer program is executed.
The fourth object of the present application is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the above-described test method of noise reduction headphones.
In summary, the present application includes at least one of the following beneficial technical effects:
1. when the noise reduction algorithm in the noise reduction earphone is tested, corresponding noise simulation data are obtained according to scene test types, noise of different scenes can be simulated, noise reduction tests are conducted, and therefore the noise scenes of the different scenes can be subjected to targeted noise reduction, meanwhile, when the noise reduction algorithm is tested, a corresponding earphone noise reduction model is generated according to test results, namely the noise reduction algorithm, so that the suitability of the noise reduction algorithm and each earphone can be improved, the richness of the noise simulation data can be improved through the mode of continuously enriching the scene test types, and the adaptation degree of the earphone to the environment is improved when the noise reduction earphone is used by a user in a follow-up mode, so that the noise reduction effect of the earphone is improved;
2. because the related noise reduction algorithm consumes larger power consumption when the noise reduction earphone is used for noise reduction, especially the use time of the Bluetooth earphone is influenced, the next scene is tested by the correction result after the noise reduction algorithm model is corrected one by one, and the applicable correction result is used as the noise reduction algorithm of the scene at the same time, so that the number of the noise reduction algorithms in the model of the whole noise reduction algorithm can be reduced, the noise reduction algorithm in the noise reduction process is simplified, and the power consumption of the noise reduction earphone consumed when the noise reduction earphone is used can be reduced;
3. by means of packaging the noise simulation data and associating the noise scene recognition model with the corresponding noise simulation data, the noise scene recognition model and the noise reduction algorithm can be separated, scene recognition calculation can be performed through a terminal connected with the noise reduction earphone through the noise scene recognition model, calculation of the noise reduction earphone in the aspect of noise reduction is further reduced, and therefore use power consumption of the noise reduction earphone is reduced.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings.
In an embodiment, as shown in fig. 1, the application discloses a method for testing a noise reduction earphone, which specifically includes the following steps:
s10: and acquiring a scene test type and a noise reduction algorithm model, and acquiring corresponding noise simulation data according to the scene test type.
In the present embodiment, the scene test type refers to a type in which a headset use environment is recorded. Noise simulation data refers to data of simulation noise for each type of scene. The noise reduction algorithm model refers to an initial algorithm that reduces noise.
Specifically, in order to pertinently reduce noise in different scenes during the subsequent use of the noise reduction earphone, an initial algorithm for performing noise reduction processing on noise is acquired first, as a model of the noise reduction algorithm, and by acquiring noise data corresponding to each scene test type as the noise simulation data by acquiring the user on different application scenes using the noise reduction earphone, such as indoor, outdoor, market, various vehicles, and other scenes.
S20: and acquiring audio output reference data, inputting noise simulation data into a noise reduction algorithm model one by one according to the scene test type for testing, and comparing the acquired audio noise reduction result with the audio output reference data respectively to obtain a noise reduction test result.
In this embodiment, the audio output reference data refers to audio data output from the headphones after noise is reduced. The noise reduction test result is the result of the difference between each noise simulation data and the corresponding audio output reference data after the noise reduction algorithm model is used for reducing the noise.
Specifically, from the viewpoint of use of the user, the noise reduction effect of using the noise reduction earphone in each scene is uniform, that is, it is desirable to eliminate noise as much as possible no matter whether the noise reduction earphone is used in any scene, so as to improve the effect of audio playing or be in a quiet state, therefore, after noise reduction is performed on the noise, the data of the audio output meeting the user noise reduction requirement is preset and used as the audio output reference data.
Further, the noise simulation data of each scene test type are input into a noise reduction algorithm model, the noise reduction effect of each scene is obtained through an initial noise reduction algorithm, the noise reduction effect is respectively compared with the audio output reference data, and differences between the noise simulation data and the audio output reference data are used as noise reduction test results after noise in each scene is reduced through a basic noise reduction algorithm of a test primary examination.
S30: and respectively correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type.
Specifically, according to the noise reduction algorithm model, that is, the effect of the initial noise reduction algorithm after the noise reduction is performed on each piece of noise simulation data, the noise reduction algorithm model is respectively corrected, that is, each piece of noise simulation data is respectively corrected in a targeted manner aiming at the noise reduction algorithm model, so that each piece of noise simulation data noise reduction algorithm is obtained, that is, each piece of noise reduction algorithm is corrected by the noise reduction algorithm model according to the noise reduction test result, and the corresponding noise simulation data can be subjected to noise reduction, so that the audio output standard is achieved.
S40: and generating an earphone noise reduction model according to each noise reduction algorithm, and generating model test information according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
Specifically, the noise reduction algorithm and the corresponding noise simulation data are packaged, so that a corresponding earphone noise reduction model is obtained, and then when the noise reduction earphone is used for noise reduction, the use scene of a user can be firstly identified through the earphone noise reduction model, namely, the noise environment in which the user is currently located is judged through the acquired noise data of the scene, and the corresponding noise reduction algorithm is further acquired for noise reduction.
Further, according to each type of noise reduction earphone, further test simulation is conducted on the earphone noise reduction model, so that the earphone noise reduction model is better matched with each noise reduction earphone.
In one embodiment, as shown in fig. 2, in step S20, audio output reference data is acquired, noise simulation data is input into a noise reduction algorithm model one by one according to a scene test type for testing, and the acquired audio noise reduction result is compared with the audio output reference data respectively to obtain a noise reduction test result, which specifically includes:
s21: and extracting noise characteristic data corresponding to each piece of noise simulation data, and respectively inputting each piece of noise characteristic data into the noise algorithm model.
In the present embodiment, the noise characteristic data refers to an audio characteristic in each noise analog data.
Specifically, before inputting the noise simulation data to the noise algorithm model, the corresponding noise feature is extracted from the noise simulation data as noise feature data, and the noise feature data is input to the noise algorithm model.
S22: and acquiring an audio noise reduction result corresponding to each noise characteristic data from the noise algorithm model, and extracting each audio noise reduction result to extract the audio noise reduction characteristic data.
Specifically, after the noise characteristic data is input into the noise algorithm model, the noise algorithm model outputs a corresponding audio noise reduction result, and corresponding characteristics are extracted from the noise result to obtain the audio noise reduction characteristic data.
S23: and extracting audio reference features from the audio output reference data, and respectively comparing the audio reference features with each audio noise reduction feature to obtain a noise reduction test result corresponding to each noise feature data.
Specifically, the characteristics of the audio output reference data are extracted and used as the audio reference characteristics, each audio noise reduction characteristic is respectively compared with the audio reference characteristics, and the corresponding comparison result is used as a noise reduction test result.
In one embodiment, as shown in fig. 3, in step S23, namely, extracting audio reference features from audio output reference data, and comparing the audio reference features with each audio noise reduction feature to obtain a noise reduction test result corresponding to each noise feature data, specifically including:
s231: constructing a reference feature vector of the audio reference features and constructing a noise reduction feature vector of each audio noise reduction feature.
Specifically, by means of constructing feature vectors, corresponding feature vectors are constructed for the audio reference feature and each audio noise reduction feature, so that the reference feature vectors and noise reduction feature vectors corresponding to each noise simulation data are obtained.
S232: and calculating comparison distance data of each noise reduction feature vector and the reference feature vector, and taking the comparison distance data as a noise reduction test result.
Specifically, by means of calculating vectors, vector distances between each noise reduction feature vector and the reference feature vector are calculated, comparison distance data are obtained, and therefore differences between each audio noise reduction result and the audio output reference data are judged to be used as noise reduction test results.
In one embodiment, as shown in fig. 4, in step S30, the noise reduction algorithm model is modified according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type, which specifically includes:
s31: and sequencing each scene test type according to a preset rule, and correcting the noise reduction algorithm model with the first sequencing to obtain a first correction result.
Specifically, the scene test types are classified and ordered in advance, and similar types are classified into one type, for example, after the scenes in markets, stations and other rooms are classified into one type, all the scene test types are ordered.
Further, the noise reduction algorithm model corresponding to the first scene test type is corrected, namely, corresponding correction is carried out according to the corresponding noise reduction test result, so that a first correction result is obtained, and after the first noise simulation data is input into the first correction result, the obtained noise reduction result accords with the audio output reference data.
S32: and inputting the noise simulation data corresponding to the second scene test type into the first correction result to obtain correction noise data, and performing difference comparison on the correction noise data and the audio output reference data.
Specifically, the noise simulation data corresponding to the second scene test type is input into the first correction result to obtain corresponding correction noise data, and difference comparison is performed on the correction noise data in the audio output reference data according to the mode of obtaining the noise reduction test result.
S33: and if the difference comparison result is within the preset threshold value, the first correction result is used as a corresponding noise reduction algorithm, otherwise, the second noise reduction algorithm model is corrected to obtain a second correction result.
Specifically, if the difference comparison result is within the preset noise reduction error range, namely within the preset threshold, the first correction result is used as a noise reduction algorithm corresponding to the second scene, otherwise, the noise reduction test result according to the second scene is corrected, and the noise reduction algorithm model is corrected to obtain a second correction result.
S34: starting from the noise simulation data corresponding to the third scene test type, inputting the corresponding noise simulation data into the corrected results after the noise reduction algorithm model is corrected one by one, and comparing the output results with the audio output reference data in a difference mode so as to screen the corresponding corrected results or correct the noise reduction algorithm model.
Specifically, starting from the noise simulation data of the third scene test type, inputting the noise simulation data into the corrected algorithm to test, respectively obtaining corresponding noise reduction results, calculating the difference between each noise reduction result and the audio output reference data, obtaining the noise reduction result with the smallest difference, taking the corresponding correction result as the noise reduction algorithm of the scene if the noise reduction result is within a preset threshold value, and otherwise, carrying out corresponding correction on the noise reduction algorithm model according to the corresponding noise reduction test result.
In one embodiment, as shown in fig. 5, in step S40, a headset noise reduction model is generated according to each noise reduction algorithm, which specifically includes:
s41: and packaging all the noise simulation data to obtain a noise scene recognition model.
Specifically, the noise scene recognition model is obtained by packaging the noise simulation data obtained through statistics, so that the noise of the environment can be obtained in the actual use process of the noise reduction earphone, and the noise of the environment is input into the noise scene recognition model for analysis and recognition, so that the current use environment of the user is recognized, namely, the corresponding scene test type is recognized.
S42: and correlating each noise reduction algorithm with corresponding noise simulation data in the noise scene recognition model to obtain an earphone noise reduction model.
Specifically, according to the association relation between each noise reduction algorithm and the scene test type, the noise reduction algorithm is associated with noise simulation data in the noise scene recognition model, and then the earphone noise reduction model is obtained. In the actual use process, after the user connects the noise reduction earphone with the terminal for audio playing, for example, a smart phone, the noise scene recognition model is sent to the audio playing terminal, the noise of the user using environment is recognized through the audio playing terminal, the recognized result is sent back to the noise reduction earphone, the noise reduction earphone matches the corresponding noise reduction algorithm according to the recognized result to reduce the noise of the environment, and therefore the power consumption of the noise reduction earphone for recognizing the noise environment is saved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
In an embodiment, a test system of a noise reduction earphone is provided, where the test system of the noise reduction earphone corresponds to the test method of the noise reduction earphone in the above embodiment one by one. As shown in fig. 6, the test system of the noise reduction earphone includes a scene simulation module, an algorithm test module, an algorithm correction module and a model test module. The functional modules are described in detail as follows:
the scene simulation module is used for acquiring a scene test type and a noise reduction algorithm model and acquiring corresponding noise simulation data according to the scene test type;
the algorithm test module is used for acquiring audio output reference data, inputting noise simulation data into the noise reduction algorithm model one by one according to the scene test type for testing, and comparing the acquired audio noise reduction result with the audio output reference data respectively to obtain a noise reduction test result;
the algorithm correction module is used for respectively correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type;
the model test module is used for generating an earphone noise reduction model according to each noise reduction algorithm, and generating a model test message according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
Optionally, the algorithm test module includes:
the data input sub-module is used for extracting noise characteristic data corresponding to each piece of noise simulation data and respectively inputting each piece of noise characteristic data into the noise algorithm model;
the feature extraction submodule is used for acquiring an audio noise reduction result corresponding to each noise feature data from the noise algorithm model, extracting each audio noise reduction result and extracting the audio noise reduction feature data;
and the feature comparison sub-module is used for extracting audio reference features from the audio output reference data, and respectively comparing the audio reference features with each audio noise reduction feature to obtain a noise reduction test result corresponding to each noise feature data.
Optionally, the feature comparison submodule includes:
the vector construction unit is used for constructing a reference feature vector of the audio reference feature and constructing a noise reduction feature vector of each audio noise reduction feature;
the vector comparison unit is used for calculating comparison distance data of each noise reduction feature vector and the reference feature vector, and taking the comparison distance data as a noise reduction test result.
Optionally, the algorithm correction module includes:
the first correction submodule is used for sequencing each scene test type according to a preset rule, correcting the noise reduction algorithm model of the first sequencing to obtain a first correction result;
the second correction sub-module is used for inputting noise simulation data corresponding to the second scene test type into the first correction result to obtain correction noise data, and performing difference comparison on the correction noise data and the audio output reference data;
the difference comparison sub-module is used for taking the first correction result as a corresponding noise reduction algorithm if the difference comparison result is within a preset threshold value, and otherwise, correcting the second noise reduction algorithm model to obtain a second correction result;
and the cyclic correction sub-module is used for starting from the noise simulation data corresponding to the third scene test type, inputting the corresponding noise simulation data into the corrected results after the noise reduction algorithm model is corrected one by one, and comparing the output results with the audio output reference data in a difference way so as to screen the corresponding corrected results or correct the noise reduction algorithm model.
Optionally, the model test module includes:
the data packaging sub-module is used for packaging all the noise simulation data to obtain a noise scene recognition model;
and the model association sub-module is used for associating each noise reduction algorithm with the corresponding noise simulation data in the noise scene recognition model to obtain the earphone noise reduction model.
For specific limitations regarding the test system of the noise reduction earphone, reference may be made to the above limitations regarding the test method of the noise reduction earphone, and no further description is given here. The modules in the test system of the noise reduction earphone can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of testing a noise reducing earphone.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
acquiring a scene test type and a noise reduction algorithm model, and acquiring corresponding noise simulation data according to the scene test type;
acquiring audio output reference data, inputting noise simulation data into a noise reduction algorithm model one by one according to a scene test type for testing, and respectively comparing the acquired audio noise reduction result with the audio output reference data to obtain a noise reduction test result;
correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type;
and generating an earphone noise reduction model according to each noise reduction algorithm, and generating model test information according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a scene test type and a noise reduction algorithm model, and acquiring corresponding noise simulation data according to the scene test type;
acquiring audio output reference data, inputting noise simulation data into a noise reduction algorithm model one by one according to a scene test type for testing, and respectively comparing the acquired audio noise reduction result with the audio output reference data to obtain a noise reduction test result;
correcting the noise reduction algorithm model according to each noise reduction test result to obtain a noise reduction algorithm corresponding to each scene test type;
and generating an earphone noise reduction model according to each noise reduction algorithm, and generating model test information according to the earphone noise reduction model so as to carry out adaptive adjustment according to different noise reduction earphones.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.