Offline package update method, device, equipment and medium based on user behavior

Abstract

The present invention discloses an offline package update method, device, equipment and medium based on user behavior. When the method is applied to the program end, it includes: obtaining a module update list sent by the server end, wherein the module update list is an update list screened by the server in a pre-stored offline package according to a user behavior prediction path; obtaining a module update package on the server end according to the module update list; updating the corresponding functional modules in the pre-stored offline package of the program end through a preset cache replacement algorithm and the module update package. When the method is applied to the server end, it includes: obtaining a pre-stored offline package and user behavior data sent by the program end; performing behavior analysis and prediction on the user behavior data to generate a user behavior prediction path; screening out a module update list in the pre-stored offline package according to the user behavior prediction path and sending it to the program end. By implementing the method of the present invention, the problem that the offline package cannot be flexibly adjusted dynamically according to user behavior in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of software update management, and in particular to a user behavior-based offline package update method, device, equipment and medium. Background Art

[0002] With the rapid development of mobile Internet of Things technology, the widespread popularity of mobile applications has greatly improved the convenience of life and work. With the expansion of functions and the diversification of user needs, offline package technology has emerged, allowing key resources to be pre-packaged to user devices to reduce startup and access delays. In an environment with no network or weak network, offline packages can ensure smooth operation of applications. However, although the current offline package technology has alleviated the problems caused by network delays and bandwidth limitations to a certain extent, it has also exposed problems such as delayed content updates, insufficient flexibility, and redundant resource usage, making it difficult for existing offline packages to be flexibly adjusted dynamically according to user behavior. Summary of the invention

[0003] The embodiments of the present invention provide a method, apparatus, device and medium for updating an offline package based on user behavior, aiming to solve the problem in the prior art that the offline package cannot be flexibly adjusted dynamically according to user behavior.

[0004] In a first aspect, an embodiment of the present invention provides an offline package update method based on user behavior, which is applied to a program end, and includes: obtaining a module update list sent by a server end, wherein the module update list is a module update list screened out by the server from an offline package pre-stored on the server end according to a user behavior prediction path; obtaining a corresponding module update package on the server end according to the module update list; and updating the corresponding functional module in the offline package pre-stored on the program end through a preset cache replacement algorithm and the module update package.

[0005] In the second aspect, an embodiment of the present invention provides an offline package update method based on user behavior, which is applied to the server side, and includes: obtaining a pre-stored offline package and user behavior data sent by the program side; performing behavior analysis and prediction on the user behavior data through a preset behavior analysis method to generate a user behavior prediction path; filtering out a module update list in the pre-stored offline package according to the user behavior prediction path; and sending the module update list to the program side.

[0006] In a third aspect, an embodiment of the present invention further provides an offline package update device based on user behavior, which includes a unit for executing any method as described in the first aspect, or includes a unit for executing any method as described in the second aspect.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0008] In a fifth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and wherein the program instructions can implement the above method when executed by a processor.

[0009] The embodiment of the present invention provides an offline package update method, device, equipment and medium based on user behavior. When the method is applied to the program end, the module update list sent by the server end is obtained, wherein the module update list is the module update list screened out by the server from the pre-stored offline package on the server end according to the user behavior prediction path; the corresponding module update package is obtained from the server end according to the module update list; the corresponding functional module in the pre-stored offline package on the program end is updated by the preset cache replacement algorithm and the module update package. When the method is applied to the server end, the pre-stored offline package and user behavior data sent by the program end are obtained; the user behavior data is analyzed and predicted by the preset behavior analysis method to generate a user behavior prediction path; the module update list is screened out from the pre-stored offline package according to the user behavior prediction path; and the module update list is sent to the program end. The embodiment of the present invention generates a user behavior prediction path by analyzing and predicting the user's behavior, so as to screen the functional modules that the user may access frequently from the pre-stored offline package according to the user's behavior prediction path, thereby personalizing the customized module update list. And according to the module update package corresponding to the module update list, the corresponding functional modules in the pre-stored offline package will be updated to achieve accurate and personalized customization of the offline package content according to user behavior, thereby effectively and flexibly updating the content in the offline package according to user behavior, so as to improve the user experience while providing users with a smoother and personalized application experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0011] Figure 1 A flowchart of the offline package update method based on user behavior provided by an embodiment of the present invention applied to a program terminal;

[0012] Figure 2A schematic diagram of a sub-process of applying the offline package update method based on user behavior provided by an embodiment of the present invention to a program terminal;

[0013] Figure 3 A schematic diagram of a sub-process of applying the offline package update method based on user behavior provided by an embodiment of the present invention to a program terminal;

[0014] Figure 4 A schematic diagram of a flow chart of an offline package updating method based on user behavior provided in an embodiment of the present invention applied to a server side;

[0015] Figure 5 A schematic diagram of a sub-process of applying the offline package update method based on user behavior provided in an embodiment of the present invention to a server side;

[0016] Figure 6 A schematic diagram of a sub-process of applying the offline package update method based on user behavior provided in an embodiment of the present invention to a server side;

[0017] Figure 7 A schematic diagram of a sub-process of applying the offline package update method based on user behavior provided in an embodiment of the present invention to a server side;

[0018] Figure 8 A schematic block diagram of an offline package updating device based on user behavior provided by an embodiment of the present invention applied to a program terminal;

[0019] Fig. 9 A schematic block diagram of an offline package updating device based on user behavior provided by an embodiment of the present invention applied to a server;

[0020] Fig.10 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] See also Figure 1 , Figure 1 A flowchart of the offline package update method based on user behavior provided in an embodiment of the present invention. The offline package update method based on user behavior in this embodiment can be applied to the process of pre-packaging and storing the key resources of an application. For example, the static resources in the pages of the application such as HTML, JavaScript, CSS, etc. can be pre-packed by the method of the present invention. The use of this method to package and update offline packages can ensure the real-time and pertinence of the offline package content, effectively reduce unnecessary data transmission and storage, thereby achieving efficient use and intelligent management of resources while improving the user experience.

[0026] Figure 1 The figure is a flow chart of the offline package update method based on user behavior provided by an embodiment of the present invention applied to the program terminal. As shown in the figure, the method includes the following steps S110-S130.

[0027] S110, obtaining a module update list sent by the server, wherein the module update list is a module update list selected by the server from an offline package pre-stored on the server according to a user behavior prediction path.

[0028] In this embodiment, the offline package is a resource package generated by packaging static resources in the page including HTML, JavaScript, CSS, etc. into a compressed package. The server side receives the pre-stored offline package and user behavior data sent by the program side, and uses big data analysis and machine learning algorithms to deeply analyze the user behavior data, thereby analyzing user preferences, usage habits and potential needs to generate a user behavior prediction path, wherein the user behavior prediction path is the path predicted by the server side to be clicked when the user uses the application. According to the user behavior prediction path, a list of functional modules that need to be updated in the pre-stored offline package is filtered out to form a module update list. The program side receives the module update list sent by the server side. By obtaining the module update list sent by the server side, the functional modules that need to be updated in the pre-stored offline package can be understood according to user behavior, thereby ensuring the real-time and pertinence of the offline package content.

[0029] S120: Obtain a corresponding module update package from the server according to the module update list.

[0030] In this embodiment, the program end obtains the corresponding module update package from the server end according to the module update list when the program is started. Specifically, when the program is started, a module update request is sent to the offline package sending platform of the server end, and then the module update package sent by the platform is received. By obtaining the corresponding module update package from the server end according to the module update list, the corresponding functional module can be updated according to the module update package, thereby reducing unnecessary data transmission and storage.

[0031] In one embodiment, if Figure 2 As shown, the step S120 also includes steps S121-S122.

[0032] S121, detecting whether there is a user access record for the function module in the low priority update list;

[0033] S122: If the user access record exists, obtain a corresponding low-priority module update package from the server.

[0034] In this embodiment, the module update list includes a high priority update list and a low priority update list, wherein the high priority update list is a list of user commonly used function modules screened out by the server from the pre-stored offline package on the server side according to the user behavior prediction path, and the low priority update list is a list of modules to be updated generated by the server side according to whether there are modules to be updated in the pre-stored offline package. In the program use phase, it is monitored whether the function modules in the low priority update list are accessed by the user. If it is detected that there is a user access record for the function module in the low priority update list, it means that it has been accessed by the user. In order to avoid the user browsing the corresponding function module when offline, the corresponding low priority module update package is obtained on the server side, and if no user access record is detected, the corresponding low priority module update package is not obtained. By detecting whether there is a user access record for the function module in the low priority update list, if there is, the corresponding low priority module update package is obtained on the server side, so as to determine whether to update the low priority module according to the user access situation, thereby improving the pertinence of the offline package and effectively improving the user experience while reducing unnecessary data transmission.

[0035] S130, updating the corresponding functional modules in the offline package pre-stored on the program end through a preset cache replacement algorithm and the module update package.

[0036] In this embodiment, the preset cache replacement algorithm is an algorithm for replacing and updating the corresponding functional modules in the pre-stored offline package of the program terminal, for example, a cache replacement algorithm (LRU algorithm), which retains the most recently used data and eliminates the longest unused data. Among them, it can be understood that based on user behavior and network environment, the cache space of the program terminal is divided into multiple areas, and offline package resources of different versions of different functional modules are stored respectively. The corresponding functional modules in the pre-stored offline package of the program terminal are updated by the preset cache replacement algorithm and the module update package. For example, if the A module has three versions 1.0.0, 1.0.1, and 1.0.2 locally, if the version in the module update package is 1.0.3, then the offline resources of version 1.0.0 are deleted according to the cache replacement algorithm (LRU algorithm), and the offline resources of version 1.0.3 are saved. And an offline package file is generated according to the updated functional module and stored in the user device. The program terminal automatically updates the corresponding functional modules in the offline package through the preset cache replacement algorithm to provide users with tailored and highly relevant data resources, thereby making the user experience smoother and more natural.

[0037] In one embodiment, if Figure 3 As shown, the step S130 also includes steps S131-S133.

[0038] S131, detecting the update package type of the received module update package;

[0039] S132, if the update package type is a differential package, performing a patch upgrade on the corresponding functional module;

[0040] S133: If the update package type is a full package, the corresponding functional module is replaced as a whole according to the version number and the preset storage threshold.

[0041] In this embodiment, the differential package is a file type containing the difference between the new version and the old version; the full package is a complete software package, which contains all the source files, library files, configuration files and resource files of the software, and is usually used for the initial installation and major version upgrade of the software. After receiving the module update package, the program terminal detects the update package type. If the module update package is a differential package, the corresponding functional module is patched and upgraded according to the differential package. If the update package is a full package, the corresponding functional module is replaced as a whole according to the version number and the preset storage threshold. Specifically, the program terminal stores multiple versions of offline packages, that is, each version of the functional module has its corresponding version number. The preset storage threshold is the threshold for offline package version storage set locally by the program terminal. Specifically, if the preset storage threshold is set to store only three versions of offline packages, and two versions of offline packages have been stored, when the module update package is a full package, the full package can be directly stored. If three versions of offline packages have been stored, the oldest version of the offline package is replaced according to the version number to update the offline package through the full package. By performing patch upgrades or overall updates on the offline package in a targeted manner according to the type of the module update package, the content of the offline package can be dynamically adjusted, thus avoiding ineffective occupation and waste of resources.

[0042] Figure 4 The figure is a flow chart of the offline package updating method based on user behavior provided by an embodiment of the present invention applied to the server side. As shown in the figure, the method includes the following steps S210-S240.

[0043] S210, obtaining the pre-stored offline package and user behavior data sent by the program terminal.

[0044] In this embodiment, the server receives the pre-stored offline package and user behavior data sent by the program, wherein the user behavior data is the user usage record collected by the program, such as application usage record, page access history, user interaction behavior and other data, and the pre-stored offline package is the key resources pre-packaged and stored on the user's device by the program, such as HTML, JavaScript, CSS and other static resources in the page. The server receives the pre-stored offline package and user behavior data sent by the user, so as to analyze and predict the user behavior data, and selects the corresponding update list in the pre-stored offline package to ensure that the content in the offline package is always closely aligned with the actual needs of the user.

[0045] S220: Performing behavior analysis and prediction on the user behavior data using a preset behavior analysis method to generate a user behavior prediction path.

[0046] In this embodiment, the preset behavior analysis method is a method for analyzing and predicting the user behavior data. For example, the preset behavior analysis method can be a big data analysis algorithm or a machine learning algorithm that can deeply analyze user behavior data to analyze user preferences. The user behavior data is analyzed and predicted by the preset behavior analysis method to generate a user behavior prediction path. For example, the preset behavior analysis method predicts that the user will access the question-and-answer related modules in the community module and view the historical reply messages in the message module between 9 and 11 in the morning. Then a user behavior prediction path for the user to access the community module and the message module is generated. By performing behavior analysis and prediction on the user behavior data to generate a user behavior prediction path, it is ensured that the offline package contains the data and functions that the user is most likely to access, thereby improving the user experience and the accuracy of data access.

[0047] In one embodiment, if Figure 5 As shown, the step S220 also includes steps S221-S224.

[0048] S221, pre-processing the user behavior data by cleaning, deduplication, and formatting them in a unified format;

[0049] S222, analyzing the preprocessed user behavior data by using a preset moving average model to predict the user behavior path;

[0050] S223, processing the processed user behavior data by a preset association rule algorithm to obtain an association module of the user behavior path;

[0051] S224: Determine the user behavior prediction path according to the user behavior path and its corresponding association module.

[0052] In this embodiment, the server side cleans, de-duplicates and pre-processes the user behavior data in a unified format. Specifically, the user behavior data is cleaned by data cleaning methods such as missing value processing and outlier processing to delete abnormal data, wherein the specific data cleaning method is not limited, and the accuracy and integrity of the data can be ensured. The cleaned data is de-duplicated to delete duplicate data, and the format of the data is unified. For example, the time data on the Android side and the iOS side are unified in format to avoid inaccurate data analysis results due to inconsistent data formats. The pre-processed user behavior data is analyzed and predicted by a preset moving average model, wherein the preset moving average model is a statistical model applied to time series analysis, specifically an autoregressive integrated moving average model, which combines three methods of autoregression, difference and moving average, and can model and predict non-stationary time series data. The preset association rule algorithm is an algorithm for discovering hidden relationships or connection rules between items in a data set. Specifically, the user behavior data can be mined through the Association Rule Mining (ARM) algorithm to obtain the associated modules of the user behavior path. For example, if it is found that the user has a high probability of clicking on the B function module after clicking on the A function module, then B is the associated module of A. The user behavior prediction path is determined according to the user behavior path and its corresponding associated module. Specifically, the user behavior path and its corresponding associated module can be used together to form the user behavior prediction path. By analyzing and predicting the user behavior data, the offline package can be updated in a targeted manner.

[0053] S230: Filter out a module update list from the pre-stored offline package according to the user behavior prediction path.

[0054] In this embodiment, the server side selects corresponding modules from the pre-stored offline package according to the functional modules involved in the user behavior prediction path to form the module update list, wherein the pre-stored offline package is sent to the server side by the program side. By selecting the module update list from the pre-stored offline package according to the predicted user behavior prediction path, it can be ensured that the offline package contains the data and functions that the user is most likely to access, thereby improving the user experience and the accuracy of data access.

[0055] In one embodiment, if Figure 6 As shown, the step S230 also includes steps S231-S233.

[0056] S231, filtering out a corresponding high-priority update list from the pre-stored offline package according to the user behavior prediction path;

[0057] S232, generating a low-priority update list according to the functional modules that need to be updated in the pre-stored offline package;

[0058] S233: Generate the module update list according to the high priority update list and the low priority update list.

[0059] In this embodiment, the module update list includes a high priority update module and a low priority update module, wherein the high priority update module is screened in the pre-stored offline package according to the user behavior prediction path. For example, if the user behavior prediction path shows that the user often clicks on the A function module and then clicks on the B function module, the high priority update list includes the A function module and the B function module; the low priority update list is generated according to the function modules that need to be updated in the pre-stored offline package. For example, the C and D function modules are function modules that are not frequently used by the user, but the server detects the new version of the C and D function modules, and then generates a low priority update list with the C and D function modules. The high priority update list and the low priority update list are combined and marked to generate the final module update list. By generating the corresponding module update list according to the user behavior prediction path and the version update situation, the program end can flexibly perform offline package update processing, and the invalid occupation and waste of resources are also avoided to a great extent.

[0060] S240, sending the module update list to the program terminal.

[0061] In this embodiment, the server side sends the module update list to the program side. Specifically, before sending the module update list, the user behavior analysis data, offline package module update list and other information are stored in the server database for subsequent analysis and optimization. In addition, the server side provides an API interface (Application Programming Interface). The API interface allows external systems or applications to query and use parsing results and cached data to achieve data sharing and collaborative work. By sending the module update list to the program side, the program side can update the offline package according to the module update list, so that the offline package can be dynamically updated to adapt to different user needs and changes in the network environment.

[0062] In one embodiment, if Figure 7 As shown, the step S240 also includes steps S2401-S12403.

[0063] S2401. If a module update request sent by a program terminal is received, the latest version number of the module to be updated is obtained, wherein the module update request includes the module to be updated;

[0064] S2402: Determine whether the difference between the latest version number and the version number of the corresponding functional module in the pre-stored offline package is greater than a preset version difference value;

[0065] S2403: If the difference is less than the preset version difference value, a module update package of a differential package type is sent using a preset differential algorithm; otherwise, a module update package of a full package type is sent.

[0066] In this embodiment, the program end sends a corresponding module update request to the server end when the program is started or after the user clicks on the low-priority update module. When the server end receives the module update request sent by the program end, it obtains the latest version number of the module to be updated, wherein the module update request includes the module to be updated, and determines whether the difference between the latest version number of the module to be updated and the version number of the corresponding functional module in the pre-stored offline package is greater than a preset version difference value, wherein the preset version difference value can be set according to a specific program and is not limited to this. In this embodiment, the preset version difference value is three version difference values. If it is less than the preset version difference value, a module update package of a differential package is sent through a preset differential algorithm. The corresponding functional module can be patched and upgraded through a module update package of a differential package type. Specifically, the preset differential algorithm is an algorithm for generating incremental data (i.e., a patch file) of a new version relative to an old version, for example, bsdiff (delta encoding, binary incremental encoding) algorithm, through the bsdiff algorithm, the new and old binary files can be compared to find the differences between them, and these differences and necessary control information can be packaged into a patch file to generate a module update package. If it is greater than the preset version difference value, a module update package of the full package type will be issued. Because the version difference is too large, there are too many patches that need to be repaired. Therefore, in order to quickly update, a full package can be issued to replace the corresponding functional module as a whole. By judging whether the difference between the latest version number and the version number of the corresponding functional module in the pre-stored offline package is greater than the preset version difference value, different types of module update packages are issued, thereby realizing rapid update and differentiated transmission of offline packages and reducing the amount of data transmission.

[0067] Corresponding to the above offline package update method based on user behavior, the present invention also provides an offline package update device based on user behavior. The offline package update device based on user behavior includes a unit for executing the above offline package update method based on user behavior, and the device can be configured in a desktop computer, tablet computer, laptop computer, etc. Figure 8 is a schematic block diagram 300 of an offline package updating device based on user behavior provided by an embodiment of the present invention applied to a program terminal. Fig. 9It is a schematic block diagram 400 of an offline package updating device based on user behavior provided by an embodiment of the present invention applied to a server side.

[0068] like Figure 8 As shown, the offline package updating device based on user behavior, when applied to a program terminal, includes a list obtaining unit 310 , an update package obtaining unit 320 , and an updating unit 330 .

[0069] The list acquisition unit 310 is used to acquire the module update list sent by the server, wherein the module update list is a module update list screened by the server from an offline package pre-stored on the server according to a user behavior prediction path.

[0070] The update package acquisition unit 320 is used to acquire the corresponding module update package from the server according to the module update list.

[0071] In one embodiment, the update package obtaining unit 320 includes a record detection unit and an obtaining sub-unit.

[0072] A record detection unit, used to detect whether there is a user access record for the function module in the low priority update list;

[0073] The acquisition subunit is used to acquire the corresponding low-priority module update package from the server side if the user access record exists.

[0074] The updating unit 330 is used to update the corresponding functional modules in the offline package pre-stored in the program terminal through a preset cache replacement algorithm and the module update package.

[0075] In one embodiment, the updating unit 330 includes a type detecting unit, a patching unit, and a replacing unit.

[0076] a type detection unit, configured to detect an update package type of the received module update package;

[0077] A patch unit, used for performing a patch upgrade on the corresponding functional module if the update package type is a differential package;

[0078] The replacement unit is used to replace the corresponding functional module as a whole according to the version number and the preset storage threshold if the update package type is a full package.

[0079] like Fig. 9 As shown, the offline package updating device based on user behavior, when applied to the server side, includes a list acquisition unit 410 , a prediction unit 420 , a screening unit 430 and a sending unit 440 .

[0080] The acquisition unit 410 is used to acquire the pre-stored offline package and user behavior data sent by the program terminal.

[0081] The prediction unit 420 is used to perform behavior analysis and prediction on the user behavior data by using a preset behavior analysis method to generate a user behavior prediction path.

[0082] In one embodiment, the prediction unit 420 includes a preprocessing unit, an analyzing unit, a correlating unit, and a determining unit.

[0083] A preprocessing unit, used for preprocessing the user behavior data by cleaning, deduplication and formatting;

[0084] An analysis unit, used to analyze the pre-processed user behavior data through a preset moving average model to predict the user behavior path;

[0085] An association unit, used to process the processed user behavior data through a preset association rule algorithm to obtain an association module for the user behavior path;

[0086] A determination unit is used to determine the user behavior prediction path according to the user behavior path and its corresponding association module.

[0087] The screening unit 430 is used to screen out a module update list in the pre-stored offline package according to the user behavior prediction path.

[0088] In one embodiment, the screening unit 430 includes a screening sub-unit, a first generating unit and a second generating unit.

[0089] A screening subunit, configured to screen out a corresponding high-priority update list from the pre-stored offline package according to the user behavior prediction path;

[0090] A first generating unit, configured to generate a low-priority update list according to the functional modules that need to be updated in the pre-stored offline package;

[0091] The second generating unit is configured to generate the module update list according to the high priority update list and the low priority update list.

[0092] The sending unit 440 is used to send the module update list to the program terminal.

[0093] In one embodiment, the sending unit 440 further includes a receiving unit, a judging unit, and a sending unit.

[0094] A receiving unit, configured to obtain the latest version number of the module to be updated if a module update request sent by the program terminal is received, wherein the module to be updated is included in the module update request;

[0095] A judging unit, used to judge whether the difference between the latest version number and the version number of the corresponding functional module in the pre-stored offline package is greater than a preset version difference value;

[0096] The sending unit is used to send a module update package of a differential package type through a preset difference algorithm if the difference value is less than the preset version difference value, otherwise send a module update package of a full package type.

[0097] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned offline package updating device 300 or 400 based on user behavior and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0098] The above-mentioned offline package updating device based on user behavior can be implemented in the form of a computer program. The computer program can be used in Fig.10 Runs on the computer device shown.

[0099] See also Fig.10 , Fig.10 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0100] See also Fig.10 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0101] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute an offline package update method based on user behavior.

[0102] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0103] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an offline package update method based on user behavior.

[0104] The network interface 505 is used to communicate with other devices over the network. Fig.10 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0105] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0106] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0107] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0108] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0109] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0110] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0111] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0112] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0114] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for updating an offline package based on user behavior, characterized in that: The method is applied to the server side, and the method comprises: Get the pre-stored offline package and user behavior data sent by the terminal; Performing behavior analysis and prediction on the user behavior data by using a preset behavior analysis method to generate a user behavior prediction path; Filtering out a module update list in the pre-stored offline package according to the user behavior prediction path; Sending the module update list to the terminal; The step of performing behavior analysis and prediction on the user behavior data by a preset behavior analysis method to generate a user behavior prediction path includes: Cleaning, deduplication and pre-processing the user behavior data into a unified format; Analyze the preprocessed user behavior data through a preset moving average model to predict the user behavior path; An association module for obtaining the user behavior path by processing the processed user behavior data through a preset association rule algorithm; Determine the user behavior prediction path according to the user behavior path and its corresponding association module; The step of filtering out a module update list from the pre-stored offline package according to the user behavior prediction path includes: Filtering out a corresponding high-priority update list from the pre-stored offline package according to the user behavior prediction path; Generate a low-priority update list according to the functional modules that need to be updated in the pre-stored offline package; The module update list is generated according to the high priority update list and the low priority update list.

2. The method according to claim 1, characterized in that After the step of sending the module update list to the program terminal, the method further comprises: If a module update request sent by the program terminal is received, the latest version number of the module to be updated is obtained, wherein the module update request includes the module to be updated; Determine whether the difference between the latest version number and the version number of the corresponding functional module in the pre-stored offline package is greater than a preset version difference value; If it is less than the preset version difference value, a module update package of the differential package type is sent through a preset differential algorithm, otherwise a module update package of the full package type is sent.

3. The method according to claim 1, characterized in that The method is applied to the program terminal, and the method includes: Acquire the module update list sent by the server, wherein the module update list is a module update list screened by the server from an offline package pre-stored on the server according to the user behavior prediction path; Obtaining a corresponding module update package from the server according to the module update list; The corresponding functional modules in the pre-stored offline package of the program terminal are updated by using a preset cache replacement algorithm and the module update package; The step of obtaining the corresponding module update package on the server side according to the module update list includes: Detecting whether there is a user access record for the function module in the low priority update list; If the user access record exists, the corresponding low-priority module update package is obtained from the server.

4. The method according to claim 3, characterized in that The step of updating the corresponding functional modules in the pre-stored offline package of the program terminal by using the preset cache replacement algorithm and the module update package includes: Detecting an update package type of the received module update package; If the update package type is a differential package, the corresponding functional module is patched and upgraded; If the update package type is a full package, the corresponding functional module will be replaced as a whole according to the version number and the preset storage threshold.

5. An offline package updating device based on user behavior, characterized in that: It comprises a program end and a server end, wherein the server end comprises a unit for executing the method as claimed in any one of claims 1-2, and the program end is a unit for executing the method as claimed in any one of claims 3-4.

6. A computer device, characterized in that: The computer device comprises a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 can be implemented.

Images