CN111770437A - Method and device for determining data card, terminal and storage medium - Google Patents
Method and device for determining data card, terminal and storage medium Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
- H04W8/20—Transfer of user or subscriber data
- H04W8/205—Transfer to or from user equipment or user record carrier
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W88/00—Devices specially adapted for wireless communication networks, e.g. terminals, base stations or access point devices
- H04W88/02—Terminal devices
- H04W88/06—Terminal devices adapted for operation in multiple networks or having at least two operational modes, e.g. multi-mode terminals
Abstract
The application belongs to the technical field of communication, and particularly relates to a method, a device, a terminal and a storage medium for determining a data card. The method for determining the data card is applied to a terminal and comprises the following steps: acquiring a neural network model corresponding to the geographic position of a terminal, and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal; acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model; determining a data master card among the first data card and the second data card based on the first network score and the second network score. Therefore, the terminal can determine the data master card in the first data card and the second data card directly based on the first network score and the second network score, and can intelligently and quickly determine the appropriate data card.
Description
Technical Field
The application belongs to the technical field of communication, and particularly relates to a method, a device, a terminal and a storage medium for determining a data card.
Background
With the development of terminal technology, more and more functions can be supported by the terminal. For example, the terminal may be a dual card dual standby terminal. The dual-card dual-standby terminal means that one terminal can simultaneously mount two data cards, and the two cards are in a standby state.
Currently, when a terminal is in a new cell or is powered on, a first data card is selected as a data master card by default. When the quality of the first data card is poor, the user can select the second data card as the data master card, and the user can watch video or browse information by using the second data card.
Disclosure of Invention
The embodiment of the application provides a method, a device, a terminal and a storage medium for determining a data card, which can determine a proper data card intelligently and quickly. The technical scheme comprises the following steps:
in a first aspect, an embodiment of the present application provides a method for determining a data card, which is applied to a terminal, and the method includes:
acquiring a neural network model corresponding to the geographic position of a terminal, and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal;
acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model;
determining a data master card among the first data card and the second data card based on the first network score and the second network score.
In a second aspect, an embodiment of the present application provides an apparatus for determining a data card, where the apparatus includes:
the model acquisition unit is used for acquiring a neural network model corresponding to the geographic position of the terminal and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal;
the score acquisition unit is used for acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model;
a data master card determining unit, configured to determine a data master card among the first data card and the second data card based on the first network score and the second network score.
In a third aspect, an embodiment of the present application provides a method for determining a data card, which is applied to a server, and the method includes:
receiving a model acquisition request sent by a terminal, wherein the model acquisition request comprises a terminal geographical position of the terminal;
the method comprises the steps of obtaining a neural network model corresponding to the geographic position of the terminal, and sending the neural network model to the terminal, wherein the neural network model is used for indicating the terminal to determine a data master card in a first data card and a second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal.
In a fourth aspect, an embodiment of the present application provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method of any one of the above first aspects when executing the computer program.
In a fifth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used for implementing any one of the methods described above when executed by a processor.
In a sixth aspect, embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application provides a method for determining a data card, a first network score of a first data card and a second network score of a second data card can be obtained by obtaining a neural network model corresponding to the geographic position of a terminal, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal, so that the terminal can determine a data master card in the first data card and the second data card directly based on the first network score and the second network score without manually selecting the data card by a user, selection steps of the data card are reduced, a proper data card can be determined intelligently and quickly, and use experience of the user can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view illustrating an application scenario of a data card determination method or a data card determination apparatus applied to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating a method for determining a data card according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating an example of a terminal interface according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating an example of a terminal interface according to an embodiment of the present application;
fig. 5 is a schematic flowchart illustrating a method for determining a data card according to an embodiment of the present application;
fig. 6 is a schematic flowchart illustrating a method for determining a data card according to an embodiment of the present application;
fig. 7 is a schematic flowchart illustrating a method for determining a data card according to an embodiment of the present application;
fig. 8 is a schematic structural diagram illustrating a data card determination apparatus according to an embodiment of the present application;
fig. 9 is a schematic flowchart illustrating a method for determining a data card according to an embodiment of the present application;
fig. 10 is a schematic flow chart illustrating a method for determining a data card according to an embodiment of the present application;
fig. 11 is a schematic structural diagram illustrating a data card determination apparatus according to an embodiment of the present application;
fig. 12 shows a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
With the rapid development of terminal technology, more and more functions can be supported by the terminal. For example, the terminal may be a dual card dual standby terminal. The dual-card dual-standby terminal means that one terminal can be simultaneously provided with two data cards, and the two cards are in a standby state. For example, the user may select a first data card in the terminal as the data master card, and at this time, the user may use the first data card to perform data service transmission. When the second data card is used as a data auxiliary card, the user can use the second data card to send a short message or make a call, and cannot use the second data card to transmit data services.
According to some embodiments, fig. 1 is a schematic view illustrating an application scenario of a data card determining method or a data card determining apparatus applied to an embodiment of the present application. As shown in fig. 1, when two cards are installed in the dual card-capable terminal, the terminal may display a dual card identification on a display screen. Both cards can be used as data cards, but only one card can be used as a data master card at the same time. When the terminal is in a new cell, the first data card is selected as the data master card by default. When the quality of the first data card is poor, the second data card can be selected as the data master card, and the user can watch video or browse information by using the second data card.
According to some embodiments, the dual cards installed in the terminal may be, for example, an a data card and a B data card. The terminal may be provided with a data master card. For example, the terminal may set the data card mounted in the card slot 1 as a data master card. When a user mounts the a data card in the card slot 1, the B data card is mounted in the card slot 2. When the terminal enters a new cell, the terminal may default to use the a data card as the currently used data card, that is, the terminal may default to use the a data card as the data master card. However, when the terminal defaults the data card a as the data master card, the network quality of the data card a defaulted as the data master card is poor compared with that of the data slave card, and the terminal network is stuck, which results in poor user experience.
Optionally, when the network quality of the first data card in the terminal is extremely poor, the terminal may send a prompt message, where the prompt message is used to prompt the user whether to switch the data card. When the user selects to switch the data cards, the user may manually replace the data cards in the terminal card slot 1, for example, the user may install the B data card in the card slot 1 and install the a data card in the card slot 2. When the terminal detects that the user replaces the data card in the terminal card slot, the terminal can determine that the B data card is the data master card, and at the moment, the terminal can use the B data card to transmit data service. However, in this process, the user needs to manually select the data card with better network quality as the data card, which results in complex data card switching operation and lower user experience. In addition, when the terminal determines the data card based on the replacement operation input by the user for the data card, the user only selects based on experience, and does not fully consider the information of the first data card and the second data card, which causes the problems that the signal instruction of the switched data card is low and the user experience is poor.
The method for determining the data card provided by the embodiment of the present application will be described in detail below with reference to fig. 2 to 7. The execution body of the embodiment shown in fig. 2-7 may be, for example, a terminal.
Referring to fig. 2, a schematic flow chart of a method for determining a data card is provided in an embodiment of the present application. As shown in fig. 2, the method of the embodiment of the present application may include the following steps S101 to S103.
S101, acquiring a neural network model corresponding to the geographic position of the terminal, and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal.
According to some embodiments, the execution subject of the embodiments of the present application is a dual card-enabled terminal. The first data card is one of the data cards in the terminal supporting the double cards. For example, the data cards installed in the terminal are a D data card and an F data card. And when the D data card is the first data card, the F data card is the second data card. And when the D data card is the second data card, the F data card is the first data card. The first data card and the second data card may be set when the terminal leaves a factory, or may be set based on a setting instruction of a user.
It is easily understood that the terminal may set the data card in the card slot 1 as the first data card based on factory settings. When the D data card is installed in the card slot 1, the D data card is a first data card, and the F data card is a second data card.
According to some embodiments, the terminal may obtain the geographical location of the terminal when the terminal is in different geographical locations. For example, the terminal can acquire the geographic position of the terminal based on a Beidou satellite positioning system, and the terminal can also acquire the geographic position of the terminal based on an auxiliary positioning system. For example, the terminal may obtain the terminal geographical location based on the smart light pole. When the terminal acquires the geographic position of the terminal, the terminal can send the geographic position of the terminal to the server and acquire the neural network model which is sent by the server and corresponds to the geographic position of the terminal.
It is easy to understand that when the terminal is located at different geographic positions, the server may monitor the geographic position of the terminal, and when the server detects that the terminal is located within the monitoring range of the server, the server may send a neural network model corresponding to the geographic position of the terminal to the terminal. The terminal can obtain the neural network model corresponding to the geographical position of the terminal.
Optionally, the first signal parameter refers to a signal parameter corresponding to the first data card, the first signal parameter does not refer to a signal parameter with a fixed value, and the first signal parameter includes, but is not limited to, a network type corresponding to the first data card, a signal quality of the base station, a spectrum bandwidth of the base station, a link quality, remaining cellular data of the first data card, and the like. The network standards include, but are not limited to, 2G, 3G, 4G, 5G, and the like. The base station Signal quality includes, but is not limited to, Received Signal Strength Indication (RSSI), Reference Signal Received Power (RSRP), Signal to NOISE RATIO (Signal NOISE RATIO, SSNR or S/N), Signal to interference RATIO (SIR), and the like. Link quality includes, but is not limited to, Transmission Control Protocol (TCP), round trip delay, and the like.
According to some embodiments, when the terminal acquires the neural network model corresponding to the geographical location of the terminal, the terminal may further acquire a first signal parameter of a first data card included in the terminal and a second signal parameter of a second data card included in the terminal. For example, the terminal may acquire a first signal parameter of the D data and a second signal parameter of the F data. The first signal parameter may be, for example, that the network standard is 4G, the signal quality of the base station is the signal-to-noise ratio, the round trip delay is 450ms, and the remaining cellular data of the first data card is 5 GB. The second signal parameter may be, for example, that the network standard is 4G, the signal quality of the base station is the signal-to-noise ratio, the round trip delay is 550ms, and the remaining cellular data of the first data card is 0.5 GB.
S102, acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model.
According to some embodiments, when the terminal acquires a first signal parameter of a first data card included in the terminal and a second signal parameter of a second data card included in the terminal, the terminal may input the first signal parameter and the second signal parameter into the neural network model, respectively, and the terminal may acquire a first network score of the first data card and a second network score of the second data card.
It will be readily appreciated that the network score may represent the network quality of the data card, with higher network scores indicating higher network quality of the data card. For example, the terminal inputs the signal parameter of the D data card into the neural network model, the first network score for the terminal to acquire the D data card may be, for example, 95 minutes, that is, the terminal inputs the network standard of the D data card into the neural network model with 4G, the base station signal quality with the signal-to-noise ratio, the round-trip delay with 450ms, and the remaining cellular data of the first data card with 5GB, and the first network score for the terminal to acquire the D data card is 95 minutes. For example, the terminal inputs the signal parameters of the F data card into the neural network model, and the second network score obtained by the terminal to the F data card may be 75 points, for example.
And S103, determining a data master card in the first data card and the second data card based on the first network score and the second network score.
According to some embodiments, the data master card refers to a data card used when the terminal performs data service transmission. When the terminal uses the data card to transmit the data service, the terminal can only use the data main card to transmit the data service. The terminal can also use the data main card and the data auxiliary card to transmit data services at the same time, and the terminal can determine the data service transmission quantity of the data main card and the data auxiliary card based on preset rules.
It is easy to understand that, the data master card of the embodiment of the present application may be, for example, a terminal that uses only the data master card for data service transmission. When the terminal acquires the first network score of the first data card and the network score of the second data card, the terminal may compare the first network score with the second network score to obtain a comparison result. The terminal may determine a data master card among the first data card and the second data card based on the comparison result. For example, the first network score of the terminal acquiring the D data card is 95 points and the second network score of the F data card is 75 points. At this time, the terminal may use the data card with the higher network score as the data master card, that is, the terminal may determine that the D data card of the D data card and the F data card is the data master card, and at this time, the terminal may use the D data card to perform data service transmission.
Optionally, when the terminal determines the data master card at the first data card and the second data card, the terminal may send a prompt message, where the prompt message is used to prompt the data card currently performing data service transmission. For example, when the terminal determines that the D data card is the data master card, the prompt message sent by the terminal may be, for example, that the D data card is the data master card, and the F data card is the data slave card. At this time, an example schematic diagram of the terminal interface may be as shown in fig. 3.
According to some embodiments, when the terminal acquires the first network score and the second network score, the terminal may calculate a difference between the first network score and the second network score. When the terminal detects that the difference value is within the preset range, the terminal can determine a data master card in the first data card and the second data card according to preset rules, the terminal can further display the first network score and the second network score on the display interface, and the data master card is determined in the first data card and the second data card based on a determination instruction input by a user. Wherein the determination instruction includes, but is not limited to, a voice determination instruction, a text determination instruction, a click determination instruction, and the like.
It is easy to understand that when the terminal obtains the network scores of both the D data card and the F data card as 95 minutes, the terminal may display the network scores on the display interface. At this time, an example schematic diagram of the terminal interface may be as shown in fig. 4. The user may determine an instruction based on the network rating input, which may be, for example, determining the D data card as a data master card.
The embodiment of the application provides a method for determining a data card, a first network score of a first data card and a second network score of a second data card can be obtained by obtaining a neural network model corresponding to a geographic position of a terminal, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal, so that the terminal can determine a data master card in the first data card and the second data card directly based on the first network score and the second network score without receiving a determination instruction of a user for the data card or manually replacing the data card in a card slot of the terminal, selection steps of the data card are reduced, a proper data card can be determined intelligently and quickly, and use experience of the user can be improved. In addition, the terminal determines the data main card based on the first network score and the second network score, so that the accuracy of data card selection can be improved, the situation that the data card network is unstable after the user determines the data main card based on experience is reduced, and the use experience of the user can be improved.
Referring to fig. 5, a schematic flow chart of a method for determining a data card is provided in an embodiment of the present application. As shown in fig. 5, the method of the embodiment of the present application may include the following steps S201 to S206.
S201, acquiring the geographic position of the terminal.
According to some embodiments, the geographical location of the terminal refers to the geographical location where the current terminal is located. The terminal can adopt a global positioning system to obtain the geographic position of the terminal, and the terminal can also adopt a Beidou satellite navigation system to obtain the geographic position of the terminal. When the terminal adopts a global positioning system or a Beidou satellite navigation system and cannot acquire the geographic position of the terminal, the terminal can adopt an auxiliary positioning system to acquire the geographic position of the terminal.
It is easy to understand that the terminal geographical location acquired by the terminal may be, for example, a Y geographical location.
S202, sending a model acquisition request containing the geographic position of the terminal to a server.
According to some embodiments, the model acquisition request refers to a request sent by the terminal to the server. When the terminal acquires the geographic position of the terminal, the terminal may generate a model acquisition request based on the geographic position of the terminal. When the terminal acquires the model acquisition request, the terminal may send the model acquisition request to the server, where the model acquisition request includes the terminal geographic location. The model obtaining request sent by the terminal may be, for example, obtaining a neural network model corresponding to the Y geographic location.
And S203, receiving the neural network model corresponding to the terminal geographic position sent by the server.
According to some embodiments, when the terminal sends a model acquisition request to the server, the server may receive the model acquisition request and acquire the geographic location of the terminal included in the model acquisition request. The server can search the neural network model corresponding to the geographical position of the terminal and send the neural network model to the terminal.
It is easy to understand that the model acquisition request sent by the terminal may be, for example, acquiring a neural network model corresponding to the Y geographic location. When the terminal sends the model acquisition request to the server, the server may find a Y neural network model corresponding to the Y geographic position in the server based on the Y geographic position, and send the Y neural network model to the terminal. The terminal can obtain a y neural network model corresponding to the geographic position of the terminal.
S204, a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal are obtained.
The specific process is as described above, and is not described herein again.
S205, acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model.
The specific process is as described above, and is not described herein again.
S206, determining the maximum network score in the first network score and the second network score, and taking the data card corresponding to the maximum network score as a data main card.
According to some embodiments, when the terminal acquires the first network score and the second network score, the terminal may determine a maximum network score of the first network score and the second network score, and use a data card corresponding to the maximum network score as a data master card. For example, when the first network score obtained by the terminal for the Q data card is 85 scores and the second network score obtained by the terminal for the W data card is 88 scores, the second network score obtained by the terminal is the maximum network score, and the terminal may use the W data card as a data master card.
According to some embodiments, please refer to fig. 6, which is a flowchart illustrating a method for switching a data card according to an embodiment of the present disclosure. As shown in fig. 6, the method of the embodiment of the present application may include the following steps S301 to S302. S301, when the first network score is larger than the second network score, the first data card is used as a data main card; s302, when the first network score is smaller than the second network score, the second data card is used as a data master card.
It is easily understood that when the terminal acquires the first network score and the second network score, the terminal may detect whether the first network score is greater than the second network score. When the terminal detects that the first network score is greater than the second network score, the terminal may use the first data card as a data master card, that is, the terminal may use the first data card to perform data service transmission. When the terminal detects that the first network score is smaller than the second network score, the terminal may use the second data card as a data master card, that is, the terminal may use the second data card for data service transmission.
Optionally, when the Q data card is a first data card and the W data card is a second data card, the first network score for the terminal to acquire the Q data card may be 88 points, the second network score for the W data card may be 85 points, for example, and the terminal may use the Q data card as a data master card. The first network score obtained by the terminal to the Q data card may be 83 points, the second network score of the W data card may be 85 points, and the terminal may use the W data card as a data master card.
Referring to fig. 7, a flow chart of a method for switching a data card according to some embodiments is shown. As shown in fig. 7, the method of the embodiment of the present application may include the following steps S401 to S402. S401, periodically acquiring a first network score and a second network score; s402, determining a data master card in the first data card and the second data card based on the first network score and the second network score.
It is easily understood that, after the terminal determines the data master card among the first data card and the second data card, the terminal may also periodically acquire the first network score and the second network score and determine the data master card among the first data card and the second data card based on the first network score and the second network score. The terminal periodically obtains the first network score and the second network score, so that the network quality of the data main card can be improved, terminal blocking is reduced, and the use experience of a user can be improved. Alternatively, the score obtaining period set by the terminal may be, for example, one hour. When the terminal determines that the Q data card is used as a data master card in the Q data card and the W data card, the terminal can obtain the first network score of the Q data card and the second network score of the W data card again after one hour. For example, when the first network score of the terminal acquiring the Q data card is 83 points and the second network score of the W data card is 85 points, the terminal may use the W data card as a data master card.
According to some embodiments, before the terminal determines the data master card among the first data card and the second data card based on the first network score and the second network score, the terminal may further determine that both the first network score and the second network score are higher than a preset score threshold. The terminal determines that the first network score and the second network score are both higher than the preset score threshold, so that the network quality of the data main card can be improved, the condition that the determined network quality of the data main card is low is reduced, and the use experience of a user can be improved.
The embodiment of the application provides a method for determining a data card, and the method can send a model obtaining request containing a terminal geographical position to a server by obtaining the terminal geographical position, and can receive a neural network model corresponding to the terminal geographical position sent by the server, so that the terminal can obtain the neural network model of the current geographical position, and the accuracy of selecting the data card can be improved. In addition, the terminal can determine the maximum network score in the first network score and the second network score by acquiring the first signal parameter of the first data card contained in the terminal and the second signal parameter of the second data card contained in the terminal, and the data card corresponding to the maximum network score is used as the data master card, so that the network quality of the data card can be improved, the selection steps of the data card are reduced, the data card does not need to be manually selected by a user, and the use experience of the user can be improved.
The following describes in detail a device for determining a data card according to an embodiment of the present application with reference to fig. 8. It should be noted that, the data card determination apparatus shown in fig. 8 is used for executing the method of the embodiment shown in fig. 2 to fig. 7 of the present application, and for convenience of description, only the portion related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to the embodiment shown in fig. 2 to fig. 7 of the present application.
Please refer to fig. 8, which shows a schematic structural diagram of a data card determining apparatus according to an embodiment of the present application. The data card determining means 800 may be implemented as all or part of the user terminal by software, hardware or a combination of both. According to some embodiments, the data card determining apparatus 800 includes a model obtaining unit 801, a score obtaining unit 802, and a data master card determining unit 803, and is specifically configured to:
the model obtaining unit 801 is configured to obtain a neural network model corresponding to a geographic location of a terminal, and obtain a first signal parameter of a first data card included in the terminal and a second signal parameter of a second data card included in the terminal;
a score obtaining unit 802, configured to obtain a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter, and the neural network model;
a data master card determining unit 803, configured to determine a data master card among the first data card and the second data card based on the first network score and the second network score.
According to some embodiments, the model obtaining unit 801, when being configured to obtain the neural network model corresponding to the geographic location of the terminal, is specifically configured to:
acquiring a geographic position of a terminal;
sending a model acquisition request containing the geographic position of the terminal to a server;
and receiving a neural network model corresponding to the geographical position of the terminal sent by the server.
According to some embodiments, the data master card determining unit 803 is configured to, when determining the data master card in the first data card and the second data card based on the first network score and the second network score, specifically:
and determining the maximum network score in the first network score and the second network score, and taking the data card corresponding to the maximum network score as a data master card.
According to some embodiments, the data master card determining unit 803 is configured to determine a maximum network score in the first network score and the second network score, and when the data card corresponding to the maximum network score is used as the data master card, the data master card is specifically configured to:
when the first network score is larger than the second network score, the first data card is used as a data main card;
and when the first network score is smaller than the second network score, the second data card is used as a data master card.
According to some embodiments, the data master card determining unit 803 is further configured to periodically obtain the first network score and the second network score, and determine the data master card among the first data card and the second data card based on the first network score and the second network score.
According to some embodiments, the data card determining apparatus 800 further includes a threshold determining unit 804, configured to determine that the first network score and the second network score are both higher than a preset score threshold before determining the data master card in the first data card and the second data card based on the first network score and the second network score.
The embodiment of the application provides a data card determining device, a model obtaining unit obtains a neural network model corresponding to a geographic position of a terminal, and obtains a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal, a score obtaining unit obtains a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model, and a data master card determining unit can determine a data master card in the first data card and the second data card based on the first network score and the second network score. Therefore, the data card determining device can determine the data main card based on the first network score and the second network score, a user does not need to manually select the data card, the selection steps of the data card are reduced, the appropriate data card can be intelligently and quickly determined, and the use experience of the user can be improved.
The following describes in detail a method for determining a data card according to an embodiment of the present application with reference to fig. 9 to 10. The execution entity of the embodiment shown in fig. 9-10 may be, for example, a server.
Fig. 9 is a schematic flow chart of a method for determining a data card according to an embodiment of the present application. As shown in fig. 9, the method of the embodiment of the present application may include the following steps S501 to S502.
S501, a model obtaining request sent by the terminal is received, and the model obtaining request comprises the terminal geographic position of the terminal.
According to some embodiments, the terminal may obtain a geographic location of the terminal and generate a model acquisition request based on the geographic location of the terminal. When the terminal generates the model acquisition request, the terminal may transmit the model acquisition request to the server. The server can receive a model acquisition request sent by the terminal.
It is easy to understand that, for example, when the terminal geographic position acquired by the T terminal is the T geographic position, the model acquisition request generated by the terminal may be, for example, to acquire a neural network model corresponding to the T geographic position, and send the model acquisition request to the server. The server may receive a model acquisition request sent by the T terminal.
And S502, acquiring a neural network model corresponding to the geographic position of the terminal, and sending the neural network model to the terminal, wherein the neural network model is used for indicating the terminal to determine a data master card in the first data card and the second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal.
According to some embodiments, when the server receives a model acquisition request sent by the terminal, the server may acquire the terminal geographical location carried in the model acquisition request. The server can obtain the neural network model corresponding to the geographical position of the terminal and send the neural network model to the terminal. Based on the neural network model, a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal, the terminal can obtain a first network score of the first data card and a second network score of the second data card, and the terminal can determine a data master card in the first data card and the second data card.
Optionally, when the server obtains the model obtaining request sent by the T terminal, the server may obtain the neural network model corresponding to the T geographic location, and send the neural network model to the T terminal. The T terminal can determine that the second data card is a data main card based on the signal parameters of the first data card, the signal parameters of the second data card and the neural network model, the first network score of the first data card is 85 scores, the network score of the second data card is 90 scores, and the T terminal can transmit data services by using the second data card.
It is easy to understand that the server can also monitor terminals of a preset range. When the server detects that the terminal exists in the preset range, the server can send the neural network model corresponding to the monitoring range to the terminal. The terminal may receive the neural network model and determine a data master card among the first data card and the second data card based on the neural network model, a first signal parameter of a first data card included in the terminal, and a second signal parameter of a second data card included in the terminal.
According to some embodiments, please refer to fig. 10, which provides a flowchart illustrating a method for switching a data card according to an embodiment of the present application. As shown in fig. 10, the method of the embodiment of the present application may include the following steps S601 to S604. S601, collecting signal parameters and user network scores of at least one terminal in a preset range corresponding to the geographic position of the terminal, wherein the signal parameters comprise at least one of a network type, base station signal quality, base station frequency spectrum bandwidth, link quality and residual cellular data of a data card contained in the terminal; s602, training an original neural network model based on the signal parameters and the user network scores to obtain the original neural network model; s603, acquiring a network score output by the original neural network model and a difference value between the network score and a preset network score by adopting a forward propagation algorithm; s604, based on the difference value, adopting a back propagation algorithm to adjust the weight coefficient in the original neural network model, and generating the trained neural network model.
It is easy to understand that, when the server acquires the neural network model corresponding to the geographic position of the terminal, the server can train to complete the neural network model. The server can acquire signal parameters and user network scores of at least one terminal in a preset range corresponding to the geographic position of the terminal, the signal parameters of the at least one terminal are used as input of the neural network model, the user network scores are used as output of the neural network, and the original neural network model is trained, wherein the original neural network model can comprise a plurality of hidden layers, and each hidden layer can output one network score. Each hidden layer may include N neurons, for example, and each neuron may correspond to a different first weight coefficient.
Optionally, when the server trains to obtain the original neural network model, the server may use a forward propagation algorithm to obtain a network score output by the original neural network model. For example, the server may set a second weighting factor for each hidden layer, based on which the server may obtain the network score output by the original neural network model. When the server obtains the network score output by the original neural network model, the server may compare the network score output by the original neural network model with a preset network score to obtain a difference value between the network score and the preset network score. The server may adjust the weight coefficients in the original neural network model based on the difference value using a back propagation algorithm to generate a trained neural network model.
The embodiment of the application provides a data card determining method, wherein a server can obtain a neural network model corresponding to the geographic position of a terminal by receiving a model obtaining request sent by the terminal, and sends the neural network model to the terminal, wherein the neural network model is used for indicating the terminal to determine a data master card in a first data card and a second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal. Therefore, the server can send the neural network model corresponding to the geographic position of the terminal to the terminal, and the efficiency of selecting the data card by the terminal can be improved. In addition, the neural network model is used for indicating the terminal to determine the data main card based on the neural network model, the first signal parameter of the first data card contained in the terminal and the second signal parameter of the second data card contained in the terminal, so that the accuracy of selection of the data card can be improved, a user does not need to manually select the data card, and the use experience of the user can be improved.
The following describes in detail a device for determining a data card according to an embodiment of the present application with reference to fig. 11. It should be noted that, the data card determination apparatus shown in fig. 11 is used for executing the method of the embodiment shown in fig. 9-10 of the present application, and for convenience of description, only the portion related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to the embodiment shown in fig. 9-10 of the present application.
Please refer to fig. 11, which shows a schematic structural diagram of a data card determining apparatus according to an embodiment of the present application. The data card determining means 1100 may be implemented as all or part of a user terminal, in software, hardware or a combination of both. According to some embodiments, the apparatus 1100 for determining a data card includes a request receiving unit 1101 and a model sending unit 1102, and is specifically configured to:
a request receiving unit 1101, configured to receive a model acquisition request sent by a terminal, where the model acquisition request includes a terminal geographic location of the terminal;
the model sending unit 1102 is configured to obtain a neural network model corresponding to a geographic location of the terminal, and send the neural network model to the terminal, where the neural network model is used to instruct the terminal to determine a data master card from among the first data card and the second data card based on the neural network model, a first signal parameter of the first data card included in the terminal, and a second signal parameter of the second data card included in the terminal.
According to some embodiments, the apparatus 1100 for determining the data card further includes a model generating unit 1103, configured to collect, before obtaining the neural network model corresponding to the geographical location of the terminal, a signal parameter and a user network score of at least one terminal within a preset range corresponding to the geographical location of the terminal, where the signal parameter includes at least one of a network type, a base station signal quality, a base station spectrum bandwidth, a link quality, and remaining cellular data of the data card included in the terminal;
training an original neural network model based on the signal parameters and the user network scores to obtain the original neural network model;
acquiring a network score output by an original neural network model and a difference value between the network score and a preset network score by adopting a forward propagation algorithm;
and based on the difference value, adopting a back propagation algorithm to adjust the weight coefficient in the original neural network model and generating the trained neural network model.
The embodiment of the application provides a data card determining device, a request receiving unit receives a model obtaining request sent by a terminal, the model obtaining request comprises a terminal geographic position of the terminal, a model sending unit can obtain a neural network model corresponding to the terminal geographic position and send the neural network model to the terminal, and the neural network model is used for indicating the terminal to determine a data master card in a first data card and a second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal. Therefore, the determining device of the data card can send the neural network model corresponding to the geographical position of the terminal to the terminal, a user does not need to manually select the data card, the appropriate data card can be rapidly and intelligently selected, and the use experience of the user can be improved.
Please refer to fig. 12, which is a schematic structural diagram of a terminal according to an embodiment of the present disclosure. As shown in fig. 12, the terminal 1200 may include: at least one processor 1201, at least one network interface 1204, a user interface 1203, memory 1205, at least one communication bus 1202.
Wherein a communication bus 1202 is used to enable connective communication between these components.
The user interface 1203 may include a Display screen (Display) and a GPS, and the optional user interface 1203 may also include a standard wired interface and a wireless interface.
The network interface 1204 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
The Memory 1205 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1205 includes a non-transitory computer-readable medium (non-transitory computer-readable storage medium). The memory 1205 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1205 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1205 may also optionally be at least one storage device located remotely from the processor 1201 described previously. As shown in fig. 12, the memory 1205 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a certain application program for a data card.
In the terminal 1200 shown in fig. 12, the user interface 1203 is mainly used for providing an input interface for a user, and acquiring data input by the user; the processor 1201 may be configured to invoke the determined application program of the data card stored in the memory 1205, and specifically perform the following operations:
acquiring a neural network model corresponding to the geographic position of the terminal, and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal;
acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model;
a data master card is determined among the first data card and the second data card based on the first network score and the second network score.
According to some embodiments, when the processor 1201 is configured to obtain the neural network model corresponding to the geographical location of the terminal, it is specifically configured to perform the following steps: acquiring a geographic position of a terminal;
sending a model acquisition request containing the geographic position of the terminal to a server;
and receiving a neural network model corresponding to the geographical position of the terminal sent by the server.
According to some embodiments, the processor 1201 is configured to, when determining the data master card among the first data card and the second data card, based on the first network score and the second network score, specifically perform the following steps: and determining the maximum network score in the first network score and the second network score, and taking the data card corresponding to the maximum network score as a data master card.
According to some embodiments, the processor 1201 is configured to determine a maximum network score of the first network score and the second network score, and when a data card corresponding to the maximum network score is taken as a data master card, to specifically perform the following steps:
when the first network score is larger than the second network score, the first data card is used as a data main card;
and when the first network score is smaller than the second network score, the second data card is used as a data master card.
According to some embodiments, the processor 1201 is further specifically configured to perform the steps of:
the first network score and the second network score are periodically obtained, and a data master card is determined in the first data card and the second data card based on the first network score and the second network score.
According to some embodiments, the processor 1201 is configured to, before determining the data master card in the first data card and the second data card, further perform the following steps based on the first network score and the second network score:
determining that the first network score and the second network score are both above a preset score threshold.
The embodiment of the application provides a terminal, a first network score of a first data card and a second network score of a second data card can be obtained by obtaining a neural network model corresponding to a geographic position of the terminal, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal, so that the terminal can determine a data master card in the first data card and the second data card directly on the basis of the first network score and the second network score without receiving a determination instruction of a user for the data card or manually selecting the data card by the user, a proper data card can be selected rapidly and intelligently, and further the use experience of the user can be improved.
An embodiment of the present application further provides a server, where the server may include: at least one server processor, at least one network interface, a user interface, a memory, at least one communication bus.
According to some embodiments, the processor of the server is configured to invoke an application of the configuration update stored in the memory and to perform in particular the following operations:
receiving a model acquisition request sent by a terminal, wherein the model acquisition request comprises a terminal geographical position of the terminal;
the method comprises the steps of obtaining a neural network model corresponding to the geographic position of a terminal, and sending the neural network model to the terminal, wherein the neural network model is used for indicating the terminal to determine a data master card in a first data card and a second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal.
According to some embodiments, before the processor of the server is configured to obtain the neural network model corresponding to the geographical location of the terminal, the processor of the server is further configured to perform the following steps:
collecting signal parameters and user network scores of at least one terminal in a preset range corresponding to the geographic position of the terminal, wherein the signal parameters comprise at least one of network standard, base station signal quality, base station frequency spectrum bandwidth, link quality and residual cellular data of a data card contained in the terminal;
training an original neural network model based on the signal parameters and the user network scores to obtain the original neural network model;
acquiring a network score output by an original neural network model and a difference value between the network score and a preset network score by adopting a forward propagation algorithm;
and based on the difference value, adopting a back propagation algorithm to adjust the weight coefficient in the original neural network model and generating the trained neural network model.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the data card determination methods as set forth in the above method embodiments.
It is clear to a person skilled in the art that the solution of the present application can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, a Field-ProgrammaBLE gate array (FPGA), an Integrated Circuit (IC), or the like.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure are intended to be included within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. A method for determining a data card is applied to a terminal, and is characterized in that the method comprises the following steps:
acquiring a neural network model corresponding to the geographic position of a terminal, and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal;
acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model;
determining a data master card among the first data card and the second data card based on the first network score and the second network score.
2. The method of claim 1, wherein the obtaining the neural network model corresponding to the geographic location of the terminal comprises:
acquiring the geographic position of the terminal;
sending a model acquisition request containing the geographic position of the terminal to a server;
and receiving the neural network model corresponding to the terminal geographic position sent by the server.
3. The method of claim 1, wherein determining a data master card among the first data card and the second data card based on the first network score and the second network score comprises:
determining the maximum network score in the first network score and the second network score, and taking the data card corresponding to the maximum network score as the data master card.
4. The method of claim 3, wherein the determining a maximum network score of the first network score and the second network score, and using a data card corresponding to the maximum network score as the data master card comprises:
when the first network score is larger than the second network score, the first data card is used as the data master card;
and when the first network score is smaller than the second network score, the second data card is used as the data master card.
5. The method according to any one of claims 1-4, further comprising:
periodically obtaining the first network score and the second network score, and determining the data master card in the first data card and the second data card based on the first network score and the second network score.
6. The method of claim 5, wherein prior to determining a data master card in the first data card and the second data card based on the first network score and the second network score, further comprising:
determining that the first network score and the second network score are both above a preset score threshold.
7. An apparatus for determining a data card, the apparatus comprising:
the model acquisition unit is used for acquiring a neural network model corresponding to the geographic position of the terminal and acquiring a first signal parameter of a first data card contained in the terminal and a second signal parameter of a second data card contained in the terminal;
the score acquisition unit is used for acquiring a first network score of the first data card and a second network score of the second data card based on the first signal parameter, the second signal parameter and the neural network model;
a data master card determining unit, configured to determine a data master card among the first data card and the second data card based on the first network score and the second network score.
8. A method for determining a data card is applied to a server, and is characterized in that the method comprises the following steps:
receiving a model acquisition request sent by a terminal, wherein the model acquisition request comprises a terminal geographical position of the terminal;
the method comprises the steps of obtaining a neural network model corresponding to the geographic position of the terminal, and sending the neural network model to the terminal, wherein the neural network model is used for indicating the terminal to determine a data master card in a first data card and a second data card based on the neural network model, a first signal parameter of the first data card contained in the terminal and a second signal parameter of the second data card contained in the terminal.
9. The method of claim 8, wherein before obtaining the neural network model corresponding to the geographical location of the terminal, further comprising:
collecting signal parameters and user network scores of at least one terminal in a preset range corresponding to the geographic position of the terminal, wherein the signal parameters comprise at least one of a network type, base station signal quality, base station frequency spectrum bandwidth, link quality and residual cellular data of a data card contained in the terminal;
training an original neural network model based on the signal parameters and the user network scores to obtain the original neural network model;
acquiring a network score output by the original neural network model and a difference value between the network score and a preset network score by adopting a forward propagation algorithm;
and based on the difference value, adopting a back propagation algorithm to adjust the weight coefficient in the original neural network model, and generating the trained neural network model.
10. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-7 when executing the computer program.
11. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of the preceding claims 1 to 7.
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| CN202010518546.1A CN111770437A (en) | 2020-06-09 | 2020-06-09 | Method and device for determining data card, terminal and storage medium |
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Application publication date: 20201013 |