CN113127774B - Content pre-caching method and device for mobile application - Google Patents

Abstract

The invention discloses a content pre-caching method and device for mobile applications. The method includes: obtaining and analyzing user itinerary information, predicting the network non-available period corresponding to the target user based on the analysis results; based on the obtained user historical behavior information, Filter the content source applications corresponding to the target user from each mobile application installed on the terminal device; configure the configuration according to the length of the network unavailable period and the time interval between the network unavailable period and the current system time. A pre-caching policy corresponding to the content source class application is provided; and the configured pre-caching policy is delivered to the content source class application. This method can automatically determine the possible network unavailability period, thereby implementing a pre-caching strategy for content source applications to achieve the purpose of automatic caching without the need for manual operation by users, greatly improving caching efficiency.

Description

Content pre-caching method and device for mobile applications

Technical field

The present invention relates to the field of Internet technology, and in particular to a content pre-caching method and device for mobile applications.

Background technique

Currently, with the increasing popularity of the Internet, more and more users are accustomed to obtaining information and contacting friends through the Internet. The Internet has become an indispensable and important content in people's daily lives. However, in some special circumstances, such as when taking an airplane or a high-speed train, the user device is in a network unavailable state due to the impact of mobile signals.

In order to cope with the above situation, most users will choose to download resource information such as audio, video or files in the terminal device in advance, and accordingly browse the pre-downloaded resource information when the network is unavailable.

However, the inventor found in the process of implementing the present invention that the above method has at least the following shortcomings: the user is required to manually download resource information, which is cumbersome and time-consuming. If the user forgets to download in advance, there will be a lack of resources for browsing during long-distance travel. content.

Contents of the invention

In view of the above problems, the present invention is proposed to provide a content pre-caching method and device for mobile applications that overcome the above problems or at least partially solve the above problems.

According to one aspect of the present invention, a content pre-caching method for mobile applications is provided, including:

Obtain and analyze user itinerary information, and predict the network unavailable period corresponding to the target user based on the analysis results;

Based on the obtained user historical behavior information, filter content source applications corresponding to the target user from various mobile applications installed on the terminal device;

Configure a pre-caching policy corresponding to the content source application according to the length of the network unavailable period and the time interval between the network unavailable period and the current system time;

Deliver the configured pre-caching policy to the content source class application.

According to another aspect of the present invention, a content pre-caching device for mobile applications is provided, including:

The acquisition module is suitable for acquiring and analyzing user itinerary information, and predicting the network unavailable period corresponding to the target user based on the analysis results;

A screening module adapted to screen content source applications corresponding to the target user from various mobile applications installed on the terminal device based on the acquired user historical behavior information;

A configuration module adapted to configure a pre-caching policy corresponding to the content source application based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time;

The delivery module is adapted to deliver the configured pre-caching policy to the content source class application.

According to another aspect of the present invention, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus. communication;

The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the content precaching method of the mobile application.

According to yet another aspect of the present invention, a computer storage medium is provided. At least one executable instruction is stored in the storage medium. The executable instruction causes the processor to perform operations corresponding to the above content precaching method for mobile applications. .

In the content pre-caching method and device for mobile applications provided by the present invention, the network unavailable period corresponding to the target user can be automatically predicted based on the obtained user itinerary information; and, the network unavailable period corresponding to the target user can be automatically predicted based on the obtained user historical behavior information. Filter the content source applications corresponding to the target user from each mobile application installed on the terminal device, so as to configure the content source application corresponding to the length of the network unavailable period and the time interval between the network unavailable period and the current system time. The corresponding precaching strategy. It can be seen that this method can automatically determine possible periods of network unavailability, thereby implementing a pre-caching strategy for content source applications to achieve the purpose of automatic caching without manual user operation, greatly improving caching efficiency.

The above description is only an overview of the technical solution of the present invention. In order to have a clearer understanding of the technical means of the present invention, it can be implemented according to the content of the description, and in order to make the above and other objects, features and advantages of the present invention more obvious and understandable. , the specific embodiments of the present invention are listed below.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be construed as limiting the invention. Also throughout the drawings, the same reference characters are used to designate the same components. In the attached picture:

Figure 1 shows a schematic flowchart of a content pre-caching method for mobile applications according to one embodiment of the present invention;

Figure 2 shows a schematic flowchart of a content pre-caching method for mobile applications according to another embodiment of the present invention;

Figure 3 shows a schematic structural diagram of a content pre-caching device for mobile applications according to yet another embodiment of the present invention;

Figure 4 shows a schematic structural diagram of an electronic device according to the present invention;

Figure 5 shows an example of a structural diagram of a mobile phone content pre-caching system for predicting user behavior;

Figure 6 shows a flow chart of a method for predicting user behavior and implementing mobile phone content pre-caching provided in an example.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a thorough understanding of the disclosure, and to fully convey the scope of the disclosure to those skilled in the art.

Figure 1 shows a schematic flow chart of a content pre-caching method for mobile applications according to an embodiment of the present invention. As shown in Figure 1, the method includes:

Step S110: Obtain and analyze the user's itinerary information, and predict the network unavailable period corresponding to the target user based on the analysis results.

Among them, the user's itinerary information can be a short message for the itinerary notification, or it can also be the itinerary memo information input by the user, etc. By analyzing the user's itinerary information, various contents such as trip start time and transportation information can be obtained. Correspondingly, based on the above information, the network unavailable period corresponding to the target user can be predicted. The network unavailable period mainly refers to the period of time when the network is completely unavailable or the network condition is poor due to the target user being in an area with poor network signal coverage.

Step S120: Based on the obtained user historical behavior information, filter content source applications corresponding to the target user from various mobile applications installed on the terminal device.

Among them, the user historical behavior information includes user operation behavior information triggered by each mobile application, such as starting the application, clicking to browse, exiting the application, etc. It may also include the user's power-on/off instruction information triggered by the terminal device, etc. In short, the present invention does not limit the specific connotation of user historical behavior information. Any information that can reflect the user's operating behavior on the terminal device is regarded as user historical behavior information.

By analyzing user historical behavior information, it is possible to determine which mobile applications installed on the terminal device closely interact with the user, thereby screening several content source applications based on the interaction time, interaction type, interaction frequency and other factors of each mobile application.

Step S130: Configure a pre-caching policy corresponding to the content source application based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time.

Specifically, the period length of the network unavailable period is used to determine the total amount of data to be cached included in the pre-caching policy. Generally, the longer the period length of the network unavailable period, the greater the total amount of data to be cached; vice versa. short. In addition, the time interval between the network unavailable period and the current system time is used to determine the cache speed included in the pre-caching policy, that is, the amount of cache per unit time. Normally, the shorter the time interval between the network unavailable period and the current system time, the faster the cache speed, and the larger the cache volume per unit time; conversely, the longer the time interval between the network unavailable period and the current system time, the slower the cache speed. , the smaller the cache amount per unit time.

In addition to the total amount of cached data and cache speed, the pre-caching policy can also include various other contents. For example, when there are multiple content source applications, the amount of data to be cached for each content source application can be configured separately. Alternatively, you can also configure the data types to be cached and so on. In short, the present invention does not limit the specific connotation of the pre-caching strategy.

Step S140: Deliver the configured pre-caching policy to the content source class application.

Specifically, the configured pre-caching policy is delivered to each content source application respectively, so that each content source application performs pre-caching processing according to the corresponding pre-caching policy, so that users can query the pre-caching during the network unavailable period. Content.

It can be seen that in the content pre-caching method for mobile applications provided by the present invention, the network unavailable period corresponding to the target user can be automatically predicted based on the acquired user itinerary information; and, the network unavailable period corresponding to the target user can be automatically predicted based on the acquired historical behavior of the user. The information filters content source applications corresponding to the target user from various mobile applications installed on the terminal device, thereby configuring content source applications based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time. Apply the corresponding precaching policy. It can be seen that this method can automatically determine possible periods of network unavailability, thereby implementing a pre-caching strategy for content source applications to achieve the purpose of automatic caching without manual user operation, greatly improving caching efficiency. .

Figure 2 shows a schematic flowchart of a content pre-caching method for mobile applications according to another embodiment of the present invention. As shown in Figure 2, the method includes:

Step S210: Obtain the received trip notification message; parse the trip notification message to determine the trip start time, trip end time, and/or vehicle information contained in the trip notification message.

Among them, itinerary notification messages generally refer to various types of information related to the user's itinerary. For example, it can be a text message prompt message received by the user after successfully booking a ticket, or it can be an itinerary memo information input by the user. When parsing the itinerary notification message, the time field, location field, transportation field, etc. contained in it are mainly analyzed based on the preset itinerary template. For example, multiple SMS reminder messages received after successful ticket booking can be stored in advance as samples to learn the format specifications of the SMS reminder messages, and then analyze the positions of the fields representing time, location and transportation in the message file. to quickly extract the above fields. Correspondingly, the trip start time, trip end time, and/or transportation information included in the trip notification message can be determined based on the parsing results.

Step S220: Predict the period start time, period end time, and/or period duration of the network unavailable period corresponding to the target user based on the trip start time, trip end time, and/or vehicle information.

Specifically, the period start time, period end time, and period duration of the network unavailable period can be obtained directly based on the trip start time and trip end time. Alternatively, when there is only a trip start time but no trip end time, the trip end time can be predicted based on the type of transportation and the distance between the start address and the destination address. In short, this embodiment can predetermine information such as the period start time, period end time, and period duration of the network unavailable period. Among them, the network unavailability period can also be called "off-grid time", that is, the period when the network is unavailable or the network status is poor.

Step S230: Based on the obtained user historical behavior information, filter content source applications corresponding to the target user from various mobile applications installed on the terminal device.

Among them, the user historical behavior information includes user operation behavior information triggered by each mobile application, such as starting the application, clicking to browse, exiting the application, etc. It may also include the user's power-on/off instruction information triggered by the terminal device, etc. In short, the present invention does not limit the specific connotation of user historical behavior information. Any information that can reflect the user's operating behavior on the terminal device is regarded as user historical behavior information.

During specific implementation, based on the obtained user historical behavior information, determine the startup frequency, startup duration, and/or startup time period of each mobile application installed on the terminal device; based on the startup frequency, startup duration, and/or startup time period of each mobile application Start the time period and filter the content source applications corresponding to the target users. For example, applications that are started more frequently and take longer to start can be filtered into content source applications. Alternatively, according to the startup time period of each mobile application, the mobile application whose startup time period matches the network unavailable period can be used as the content source application. For example, assuming that the network unavailable period is from 3 pm to 6 pm on X, month . In addition, the interaction behavior after each mobile application is launched can also be combined to determine whether the user tends to browse network content through the mobile application. If so, the mobile application is filtered into a content source application. For example, assuming that users are accustomed to obtaining online music through music mobile applications, the music mobile applications are filtered into content source applications.

Step S240: Configure a pre-caching policy corresponding to the content source application based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time.

Among them, the pre-caching policy is used to set the data volume of the network content that needs to be cached, the cache speed, the content type of the cached network content, etc.

In an optional implementation, the total amount of content data to be cached is determined based on the length of the network unavailable period; the pre-caching strategy is determined based on the total amount of content data to be cached and the startup duration of each content source application. The amount of application data to be cached corresponding to each content source type application contained in . Normally, the longer the length of the network unavailable period, the greater the total amount of data to be cached; vice versa, the shorter it is. During the specific implementation, it is considered that users may abandon reading content that they are not interested in at any time. Therefore, in order to ensure that there is still cached content available for browsing even after users abandon reading multiple times, the total playback time or browsing time of the data to be cached should be greater than The length of the period during which the network is unavailable. Among them, the playback time or browsing time of the total amount of data to be cached can be calculated based on the user's average playback speed or average reading speed determined by the user's historical behavior information. Specifically, the playback duration or browsing duration of the total amount of data to be cached can be set to a preset multiple (such as 3 times) of the period length of the network unavailable period. The value of the preset multiple can be determined based on the user's probability of abandoning reading determined by the user's historical behavior information. For example, by analyzing user historical behavior information, calculating the average number of times users abandon reading during the reading process, and then obtaining the user's probability of abandoning reading, a higher preset multiple is set for users with a high probability of abandoning reading; for users with a low probability of abandoning reading, , you can set a smaller preset multiple.

In addition, when there are multiple content source type applications, the amount of application data to be cached corresponding to each content source type application can be further set, and the sum of the application data amounts to be cached for each content source type application is equal to the total amount of data to be cached. Specifically, determine the duration ratio between the startup duration of each content source type application; calculate the duration corresponding to each content source type application based on the total amount of content data to be cached and the duration ratio between the startup duration of each content source type application. The amount of data to be cached by the application. Correspondingly, the longer the startup time of content source applications, the greater the amount of data to be cached. Alternatively, you can also calculate the data volume ratio between the network content views of each content source application; based on the total amount of content data to be cached and the data volume ratio between the network content views of each content source application, calculate and The amount of data to be cached by the application corresponding to each content source type application. Correspondingly, a content source application with a greater number of web content views will have a greater amount of data to be cached. In this way, the total amount of data to be cached can be distributed to each content source application in a reasonable proportion. For example, assuming that the total amount of data to be cached is 1000M, and the data volume ratio between the network content views or the startup time ratio between the three content source applications is 1:1:2, then for the first two content sources The amount of application data to be cached allocated to class applications is 250M, while the amount of application data to be cached to the last content source class application is 500M.

In yet another optional implementation, the unit time cache amount of each content source type application included in the pre-caching policy is set based on the time interval between the network unavailable period and the current system time. Specifically, the longer the time interval between the network unavailable period and the current system time, the smaller the unit time cache amount of each content source type application included in the set pre-caching policy, that is, the slower the caching speed; The shorter the system time interval, the greater the cache amount per unit time of each content source type application included in the set pre-caching policy, that is, the faster the caching speed.

In another optional implementation, the user preference information of the target user is determined based on the user's historical behavior data; and the cache type information included in the pre-caching policy corresponding to the content source application is set based on the user preference information. Specifically, based on user historical behavior data, the type of user preference information of each target user can be determined, such as preference for literature, history, war, science fiction, etc. Correspondingly, based on the type of user preference information of the target user, the pre-caching policy is The included cache type information corresponding to the content source type application is set to content matching the type of the user preference information. For example, for users who prefer literature, set the cache type information corresponding to the content source application to literature; for users who prefer science fiction, set the cache type information corresponding to the content source application to science fiction. By analyzing user historical behavior information, we can ensure that cached content better meets user needs.

The above-mentioned implementation methods can be used alone or in combination, and the present invention is not limited thereto.

Step S250: Deliver the configured pre-caching policy to the content source class application.

Specifically, the configured pre-caching policy is delivered to each content source application respectively, so that each content source application performs pre-caching processing according to the corresponding pre-caching policy, so that users can query the pre-caching during the network unavailable period. Content.

In order to facilitate understanding, the implementation details of the present invention are described in detail below by taking a specific example as an example:

In recent years, with the rapid development of mobile Internet, mobile phones have been released from the role of a single communication tool to provide users with a variety of entertainment services through wireless data networks and the Internet, especially with the upcoming large-scale construction of 4G networks and even With 5G network, the bandwidth of data traffic has doubled, and high bandwidth has also expanded the scope of content services. Data such as text, images, videos, etc. can be pushed to users’ mobile phones in real time and smoothly through wireless data networks, making it convenient for users to enjoy themselves anytime, anywhere. Like content. However, in some specific environments, such as during aircraft flight, high-speed rail driving, parts of highways or underground garages and other scenarios where the base station signal cannot be reached or is weak, the user's mobile phone will not be able to connect to 3G/4G/ With 5G’s high-speed network, many content that relies on instant data push cannot be used in these scenarios. At this time, on the plane, users usually set their mobile phones to flight mode to watch pre-cached content or offline games on the mobile phone. However, on high-speed trains or highways, they need to wait until they are connected to a high-speed network before they can continue to obtain content.

In order to solve the above technical problems, a video pre-caching method is proposed. Specifically, the urban public WIFI service coverage information is obtained to obtain the urban public WIFI service data set; the user's current location information and movement information are obtained through GPS, and the user is determined through the mobile terminal The current network environment of the user; predict the user's future location based on the user's current location information and mobility information, predict the user's future network environment based on the city's public WIFI service coverage information; adjust the user's video based on the user's current network environment and predicted future network environment Preload strategy and repeat the above steps until the user finishes watching the video. During a long journey, it is very embarrassing to be unable to obtain entertainment content through the mobile phone due to lack of mobile data connection or discontinuous connection, and pre-loaded content requires manual settings in advance for the APP that supports this function. Users also need to carefully select resources, which is time-consuming and labor-intensive, seriously affecting the user experience. On the other hand, it also has a negative impact on the network image of telecom operators.

In the above video pre-caching method, it is necessary to obtain the user's current location information and movement information to predict the user's future location, and predict the user's future network environment based on the city's public WIFI service coverage information. First of all, WIFI is mainly distributed in cities and cannot be used on high-speed railways, highways or in flight mode. Moreover, the security of connecting to public WIFI is poor. Mobile phone communications are very easy to be stolen, intercepted and even cause economic losses by illegal elements. Moreover, WIFI is The stability and coverage distance cannot be compared with mobile communication networks. Automatically connecting to WIFI during preloading will not only cause serious security risks, but also cannot effectively guarantee stability and application effects.

In order to solve the above problems, this example proposes a method and system to predict user behavior and realize pre-caching of mobile phone content, by real-time monitoring of the itinerary messages received by the mobile phone (itinerary messages actively pushed by relevant software; itinerary messages set by the user) , calculate and predict how long a user may be disconnected from the mobile data network. Through daily learning of the proportion and duration of users' reading of text, audio, video and other content, we can calculate and predict the proportion and duration of content that users need to preload when they are offline, and push content preloading to content sources (i.e. content source applications). Strategy (i.e. precaching strategy).

Figure 5 shows the structure diagram of the mobile phone content pre-caching system for predicting user behavior provided in this example, including an off-grid prediction module, a user behavior learning module, a pre-caching policy calculation module and a policy interaction interface pool. The functions of each module are explained below:

(1) Off-grid prediction module

The off-grid prediction module is connected to the pre-caching policy calculation module and mainly pre-judges the possible off-grid status of the user's mobile phone in the future. The off-grid prediction module can obtain the future off-grid status of the user's mobile phone from flight information, high-speed rail ticket purchase information and long-distance travel information queried by navigation software. There are three main ways to obtain:

Through real-time monitoring and analysis of short messages related to itinerary information received by mobile phones or system messages pushed to users by relevant ticket purchasing software through the operating system;

Actively push itinerary information to the off-grid prediction module through the interface through ticket purchase or navigation software;

The user sets the itinerary information through the information input interface of the off-grid prediction module.

Correspondingly, after receiving the itinerary information, the off-grid prediction module pre-judges the time period when the vehicle may be off-grid (i.e., the network unavailable period) based on keywords such as time, location, and transportation.

For example, the off-grid prediction module received the itinerary information entered by the user on February 13:

1. Take a flight from Shanghai to Beijing at 09:00-11:10 on February 15th, totaling 2 hours and 15 minutes;

2. Take the high-speed rail from Beijing to Shanghai from 06:36 to 12:40 on February 20, a total of 6 hours and 30 minutes;

After receiving the information, the off-grid prediction module analyzes the possible off-grid time of the first message as T1=2.25h and the starting date as D1=02/15, and the possible off-grid time of parsing the second message as T2=6.5h , The starting date is D2=02/20.

The off-grid prediction module pushes the parsed information to the pre-caching strategy calculation module.

(2)User behavior learning module

The user behavior learning module is connected to the pre-caching policy calculation module. It mainly collects and analyzes the user's daily reading, listening to audio and watching video behavior habits through user historical behavior information, and continuously updates and corrects the user's behavior model through learning in order to provide Users cache content they are truly interested in.

The user behavior learning module records information about content sources when users use mobile phones on a daily basis, such as the frequency of frequently used applications, and the length of time spent reading and listening to audio and video.

For example, within a period of 6 hours, statistics show that the usage time of a certain news reading software is AT1=0.5h, that of a certain electronic reading software is AT2=0.2h, that of a certain online music playing software is AT3=0.25h, and that of a certain online video playing software AT4=0.3h. Then the ratio between each content source is AT1:AT2:AT3:AT4. Among them, this ratio is used to determine the amount of application data to be cached for each content source type application.

The user behavior learning module pushes the statistical data to the pre-caching policy calculation module.

(3) Pre-caching strategy calculation module

The pre-caching policy calculation module is connected to the off-grid prediction module, the user behavior learning module and the policy interaction interface pool. The pre-caching policy calculation module receives the data pushed by the off-grid prediction module and the user behavior learning module, and performs the pre-caching strategy calculate.

For example, for the first piece of travel information, the starting date is D2, and the possible offline time is T1. The content of T1*4 duration needs to be cached. The ratio between each content source is AT1:AT2:AT3:AT4. Among them, 4 is the preset multiple mentioned above determined based on the user's probability of abandoning reading determined by the user's historical behavior information.

For example, for the second travel information, the starting date is D2 and the possible off-grid duration is T2. The content of T2*2 duration needs to be cached. The ratio between each content source is AT1:AT2:AT3:AT4. Among them, 2 is the preset multiple determined above based on the user's probability of abandoning reading determined by the user's historical behavior information.

The pre-caching policy calculation module pushes the calculated policy data to the policy interaction interface pool.

(4) Policy interaction interface pool

The policy interaction interface pool is connected to the pre-caching policy calculation module and multiple content sources (the content sources are the abbreviation of content source applications), and provides calculated pre-caching policies to the content sources respectively.

For example, for the first piece of travel information, the strategy that the policy interaction interface pool pushes for a certain news reading software is: before the date is D1, the content of AT1*4 duration needs to be cached.

For example, for the second piece of travel information, the strategy that the policy interaction interface pool pushes for a certain news reading software is: before the date is D2, the content of AT4*2 duration needs to be cached.

The application software determines the pre-caching strategy for the user's segmented content based on the user's preferences.

It can be seen that the flow chart of the method for predicting user behavior and implementing mobile content pre-caching provided in this example is shown in Figure 6, which specifically includes the following steps:

Step 1: The user behavior learning module establishes and maintains a user behavior model by analyzing the daily usage habits of users' mobile phone applications;

Step 2: The off-grid prediction module pre-judges future off-grid conditions by obtaining a variety of itinerary information;

Step 3: The pre-caching policy calculation module calculates the pre-caching policy for the information pushed by the above module;

Step 4: The policy interaction interface pool receives the policy and pushes the corresponding policy to the content source respectively;

Step 5: The content source receives the corresponding pre-caching policy and executes it.

In summary, it can be seen that this example uses the method of obtaining trip information to predict future off-grid conditions. The current video preloading method based on network environment prediction does not take into account the temporary off-grid situation caused by the lack of base station signal service or signal degradation. Moreover, the security and practicality are affected by the defects of WIFI itself, and the application effect cannot solve the actual problem. The technical solution proposed by the present invention pre-judges the possible off-grid status of the user's mobile phone in the future, combines user behavior learning to model and analyze the user's daily usage habits, and provides a strategy for the user to cache the content they are really interested in, and through The interface pushes the policy to the content source, thereby greatly improving the user's actual experience.

Figure 3 shows a schematic structural diagram of a content pre-caching device for mobile applications according to another embodiment of the present invention. As shown in Figure 3, the device includes:

The acquisition module 31 is adapted to acquire and analyze user itinerary information, and predict the network unavailable period corresponding to the target user based on the analysis results;

The screening module 32 is adapted to screen content source applications corresponding to the target user from various mobile applications installed on the terminal device based on the obtained user historical behavior information;

The configuration module 33 is adapted to configure a pre-caching policy corresponding to the content source application based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time;

The delivery module 34 is adapted to deliver the configured pre-caching policy to the content source class application.

Optionally, the acquisition module is specifically adapted to:

Get the received itinerary notification message;

Parse the trip notification message to determine the trip start time, trip end time, and/or vehicle information contained in the trip notification message;

Predict the period start time, period end time, and/or period duration of the network unavailable period corresponding to the target user based on the trip start time, trip end time, and/or vehicle information.

Optionally, the screening module is specifically suitable for:

Based on the obtained historical user behavior information, determine the startup frequency, startup duration, and/or startup time period of each mobile application installed on the terminal device;

Based on the startup frequency, startup duration, and/or startup time period of each mobile application, content source applications corresponding to the target user are screened.

Optionally, the configuration module is specifically adapted to:

Determine the total amount of content data to be cached according to the length of the network unavailable period;

According to the total amount of content data to be cached and the startup duration of each content source type application, the amount of application data to be cached corresponding to each content source type application included in the pre-caching policy is determined.

Optionally, the configuration module is specifically adapted to:

Determine the duration ratio between the startup duration of each content source application;

According to the total amount of content data to be cached and the duration ratio between the startup time of each content source type application, the amount of application data to be cached corresponding to each content source type application is calculated.

Optionally, the configuration module is specifically adapted to:

According to the time interval between the network unavailable period and the current system time, the unit time cache amount of each content source type application included in the pre-caching policy is set.

Optionally, the configuration module is specifically adapted to:

Determine the user preference information of the target user based on the user historical behavior data;

According to the user preference information, cache type information corresponding to the content source type application included in the pre-caching policy is set.

For the specific structure and working principle of each of the above modules, please refer to the description of the corresponding steps in the method embodiment, and will not be described again here.

Embodiments of the present application provide a non-volatile computer storage medium. The computer storage medium stores at least one executable instruction. The computer executable instruction can execute the content pre-caching method for mobile applications in any of the above method embodiments. .

FIG. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the electronic device.

As shown in Figure 4, the electronic device may include: a processor (processor) 402, a communications interface (Communications Interface) 404, a memory (memory) 406, and a communication bus 408.

in:

The processor 402, the communication interface 404, and the memory 406 complete communication with each other through the communication bus 408.

The communication interface 404 is used to communicate with network elements of other devices such as clients or other servers.

The processor 402 is configured to execute the program 410. Specifically, it can execute relevant steps in the above domain name resolution method embodiment.

Specifically, program 410 may include program code including computer operating instructions.

The processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; or they may be different types of processors, such as one or more CPUs and one or more ASICs.

Memory 406 is used to store programs 410. The memory 406 may include high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The program 410 can be specifically used to cause the processor 402 to perform various operations in the above method embodiment.

The algorithms and displays provided herein are not inherently associated with any particular computer, virtual system, or other device. Various general-purpose systems can also be used with teaching based on this. From the above description, the structure required to construct such a system is obvious. Furthermore, this invention is not specific to any specific programming language. It should be understood that a variety of programming languages may be utilized to implement the invention described herein, and that the above descriptions of specific languages are intended to disclose the best mode of carrying out the invention.

In the instructions provided here, a number of specific details are described. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this description.

Similarly, it is to be understood that in the above description of exemplary embodiments of the invention, in order to streamline the disclosure and aid in the understanding of one or more of the various inventive aspects, various features of the invention are sometimes grouped together into a single embodiment. figure, or its description. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will understand that modules in the devices in the embodiment can be adaptively changed and arranged in one or more devices different from that in the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method so disclosed may be employed in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of the equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features of different embodiments are meant to be within the scope of the invention. within and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

Various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will understand that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components in the electronic device according to embodiments of the present invention. The invention may also be implemented as an apparatus or apparatus program (eg, computer program and computer program product) for performing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the element claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, third, etc. does not indicate any order. These words can be interpreted as names.

Claims (9)

1. A content pre-caching method for mobile applications, including: Obtain and analyze user itinerary information, and predict the network unavailable period corresponding to the target user based on the analysis results; Based on the obtained user historical behavior information, filter content source applications corresponding to the target user from various mobile applications installed on the terminal device; Configure a pre-caching policy corresponding to the content source application according to the length of the network unavailable period and the time interval between the network unavailable period and the current system time; Deliver the configured pre-caching policy to the content source class application; Wherein, the obtaining and parsing the user's itinerary information, and predicting the network unavailable period corresponding to the target user based on the parsing results includes: Obtain the received itinerary notification message; wherein the itinerary notification message is the SMS prompt message received after the user successfully booked the ticket or the itinerary memo information input by the user; Parse the trip notification message to determine the trip start time, trip end time, and/or vehicle information contained in the trip notification message; wherein the vehicle information includes an airplane or a high-speed rail; Predict the period start time, period end time, and/or period duration of the network unavailable period corresponding to the target user based on the trip start time, trip end time, and/or vehicle information. 2. The method according to claim 1, wherein filtering content source applications corresponding to the target user from various mobile applications installed on the terminal device according to the obtained user historical behavior information includes: Based on the obtained historical user behavior information, determine the startup frequency, startup duration, and/or startup time period of each mobile application installed on the terminal device; Based on the startup frequency, startup duration, and/or startup time period of each mobile application, content source applications corresponding to the target user are screened. 3. The method according to claim 1, wherein the preset corresponding to the content source application is configured according to the period length of the network unavailable period and the time interval between the network unavailable period and the current system time. Caching strategies include: Determine the total amount of content data to be cached according to the length of the network unavailable period; According to the total amount of content data to be cached and the startup duration of each content source type application, the amount of application data to be cached corresponding to each content source type application included in the pre-caching policy is determined. 4. The method according to claim 3, wherein the amount of application data to be cached corresponding to each content source application is determined based on the total amount of content data to be cached and the startup duration of each content source application. include: Determine the duration ratio between the startup duration of each content source application; According to the total amount of content data to be cached and the duration ratio between the startup time of each content source type application, the amount of application data to be cached corresponding to each content source type application is calculated. 5. The method according to claim 3, wherein the preset corresponding to the content source application is configured according to the length of the network unavailable period and the time interval between the network unavailable period and the current system time. Caching strategies include: According to the time interval between the network unavailable period and the current system time, the unit time cache amount of each content source type application included in the pre-caching policy is set. 6. The method according to claim 1, wherein the preset corresponding to the content source application is configured according to the period length of the network unavailable period and the time interval between the network unavailable period and the current system time. Caching strategies include: Determine the user preference information of the target user based on the user historical behavior data; According to the user preference information, cache type information corresponding to the content source type application included in the pre-caching policy is set. 7. A content pre-caching device for mobile applications, including: The acquisition module is suitable for acquiring and analyzing user itinerary information, and predicting the network unavailable period corresponding to the target user based on the analysis results; A screening module adapted to screen content source applications corresponding to the target user from various mobile applications installed on the terminal device based on the acquired user historical behavior information; A configuration module adapted to configure a pre-caching policy corresponding to the content source application based on the length of the network unavailable period and the time interval between the network unavailable period and the current system time; A delivery module that delivers the configured pre-caching strategy to the content source class application; Wherein, the acquisition module is specifically adapted to: obtain the received itinerary notification message; wherein the itinerary notification message is the SMS prompt message received after the user successfully booked the ticket or the itinerary memo information input by the user; for The itinerary notification message is parsed to determine the trip start time, trip end time, and/or transportation information contained in the itinerary notification message; wherein the transportation information includes an airplane or a high-speed rail; according to the The trip start time, trip end time, and/or vehicle information predicts the period start time, period end time, and/or period duration of the network unavailable period corresponding to the target user. 8. An electronic device, including: a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface completing communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the content precaching method for mobile applications according to any one of claims 1-6. 9. A computer storage medium, at least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to execute the content pre-caching method for mobile applications according to any one of claims 1-6. corresponding operation.