A cloud application updating method, server, storage medium and device

Abstract

The present disclosure provides a method, server, storage medium and device for updating cloud applications. The method includes: obtaining network download traffic data of each application process of the detected application and a designated screen for determining whether the application has been updated, analyzing and obtaining network download traffic characteristics of each application process and screen characteristics of the designated screen; if an application process triggers an automatic application update detection event, then based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process and the screen characteristics of the designated screen, it is determined whether the application has been updated; if the application has been updated, the inspection update tool is triggered to control the application to perform the update operation. In this way, it is possible to timely discover application updates and control the application to be automatically updated, which has the advantages of high efficiency and low cost.

Description

Technical Field

[0001] The present disclosure relates to the technical field of cloud computing, and in particular to a method, server, storage medium and device for updating a cloud application. Background Art

[0002] Based on cloud streaming technology, cloud games and other applications can be run on cloud servers. The cloud servers perform screen rendering during the application operation, and collect and encode the screen and the audio generated by the application operation, and then send them to the terminal display device in the form of audio and video streams for decoding and playback. The terminal display device is used to realize human-computer interaction and upload control instructions to the cloud server application in real time.

[0003] Since applications run on cloud servers, in the scenario of large-scale deployment and operation of applications, the number of cloud servers deployed in the cloud server room for running applications is relatively large. At the same time, there will be a large number and variety of applications deployed differently on each cloud server. In order to ensure the user's functional experience, networked applications represented by online games will update the application version from time to time.

[0004] In the related technologies, there are mainly the following ways to detect application updates: 1. Pay attention to the upgrade announcements of application content providers to obtain application update information in advance; 2. Discover application update events through manual inspections by operators; 3. Monitor whether the running directory files of the application have changed to determine whether the application has been updated; 4. Collect customer information feedback through the customer service system to obtain application update information. Among the above methods, in method 1, method 2, and method 4, for the scenario of large-scale operation of the application deployed on the cloud server, the manual information collection method or manual inspection method has the problem of low efficiency and failure to detect application updates in a timely manner; the above method 3 is only effective for some applications and cannot realize update detection for all applications.

[0005] The application update method used in the related technology includes: first temporarily offline the application to be updated from the business system, and then manually control the startup of the application instances deployed on the cloud server one by one, so that each application instance executes an application version update, and after all the application deployment instances on the cloud server are updated, the application is launched from the business system. This process also requires a lot of manual input, and the update processing efficiency is low. Summary of the invention

[0006] In view of this, the present disclosure provides a cloud application update method, server, storage medium and device to achieve timely discovery of application updates, control the application to automatically update, and improve update efficiency.

[0007] Specifically, the present disclosure is achieved through the following technical solutions:

[0008] In a first aspect, an embodiment of the present disclosure provides a method for updating a cloud application, the method comprising:

[0009] Obtain network download traffic data of each application process of the detected application and a designated screen for determining whether the application has been updated, and analyze and obtain network download traffic characteristics of each application process and screen characteristics of the designated screen;

[0010] If an application process triggers an automatic application update detection event, determining whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen;

[0011] If the application is updated, the inspection and update tool is triggered to control the application to perform an update operation.

[0012] In a second aspect, an embodiment of the present disclosure provides a server, the server comprising:

[0013] An analysis module, used to obtain network download flow data of each application process of the detected application and a designated screen for determining when the application is updated, and to analyze and obtain network download flow characteristics of each application process and screen characteristics of the designated screen;

[0014] A judgment module, configured to judge whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen if an application process triggers the automatic application update detection event;

[0015] The update module is used to trigger the inspection update tool to control the application to perform an update operation if the application is updated.

[0016] In a third aspect, an embodiment of the present disclosure provides a machine-readable storage medium, on which a number of computer instructions are stored. When the computer instructions are executed, the method described in the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, comprising: a machine-readable storage medium and a processor, wherein the machine-readable storage medium stores instruction codes, and the processor communicates with the machine-readable storage medium, reads and executes the instruction codes in the machine-readable storage medium, and implements the method described in the first aspect.

[0018] In the disclosed embodiments, a cloud application update method, server, storage medium and device are provided. In the embodiments, the network download traffic data of each application process of the detected application and the designated screen for determining whether the application has been updated are obtained, and the network download traffic characteristics of each application process and the screen characteristics of the designated screen of the application are obtained by analysis; and in the case that an application process triggers an automatic application update detection event, based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, and the network download traffic characteristics of each application process that triggers the event and the screen characteristics of the designated screen of the application, it is determined whether the application has been updated; if an update has occurred, the application is controlled to perform the update operation. Furthermore, in the embodiments of the present application, it is possible to detect the occurrence of application update behavior in a timely manner, and after detecting that the application has been updated, the application is controlled to be updated, which has the positive effects of timely discovery, efficient detection and cost saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of an application scenario of a method for updating a cloud application shown in an exemplary embodiment of the present disclosure;

[0020] Figure 2 is a flowchart of an application update method shown in an exemplary embodiment of the present disclosure;

[0021] Figure 3a is a schematic diagram of network download traffic of an application shown in an exemplary embodiment of the present disclosure;

[0022] Figure 3b is a schematic diagram of a feature screen of an application shown in an exemplary embodiment of the present disclosure;

[0023] Figure 3c is a schematic diagram of a feature screen of another application shown in an exemplary embodiment of the present disclosure;

[0024] Figure 4 It is a schematic diagram of the interaction of various systems in a method for updating a cloud application shown in an exemplary embodiment of the present disclosure;

[0025] Figure 5 is a flowchart of another method for updating a cloud application according to an exemplary embodiment of the present disclosure;

[0026] Figure 6 is a structural diagram of a server shown in an exemplary embodiment of the present disclosure;

[0027] Figure 7 It is a structural schematic diagram of an electronic device shown in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0029] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0031] For some network-based applications deployed in the cloud, such as cloud games, to ensure the normal use of the application, it is necessary to meet the requirement that the application is currently the latest version; in the large-scale operation scenario of cloud applications such as cloud games, due to the large number of cloud servers that deploy applications and the large number of types of applications deployed on each cloud server, it is difficult for operators to predict the update time of each new version of each application in advance, and thus it is impossible to ensure that the versions of the same application on all cloud servers are consistent and the latest version through unified upgrades and deployments. Based on this, the embodiments of the present disclosure provide a method, server, storage medium and device for updating cloud applications.

[0032] Figure 1 is a schematic diagram of an application scenario of a method for updating a cloud application shown in an exemplary embodiment of the present disclosure; Figure 1As shown, in this embodiment, the application is deployed on each cloud server in the cloud, and each cloud server may be deployed with different types of applications, such as APP1, APP2, APP3, etc. deployed on cloud server server1; and the same application can be deployed on different cloud servers at the same time, such as application APP1 is deployed on cloud server server1, cloud server server2 and even cloud server server n; for different clients, when starting the same application, the applications to be started by different clients will be started on different cloud servers through the resource scheduling service, thereby realizing the parallel application of multiple application processes of the same application. In the embodiment of the present application, when performing update detection on a certain application, it can be determined whether the application has been updated by monitoring the network download traffic data of each application process of the application and the designated screen used to identify the update of the application. If it is determined that the application has been updated, the application is controlled to perform update operations on each deployed cloud server. In this way, it is possible to timely discover the update behavior of the application, and control the application to be automatically updated and batch updated on each cloud server; compared with the existing technology, it has the positive effects of timely update detection and cost saving.

[0033] Figure 2 This is a flowchart of a method for updating a cloud application shown in an exemplary embodiment of the present disclosure; this embodiment provides a method for updating an application, the execution subject of the method is an update server, the update server can be the above-mentioned cloud server, or other servers independent of the cloud server, and the update server can be a separate server or a server cluster, which is not limited in this application. Figure 2 As shown, the update method of the cloud application includes the following steps S10-S30:

[0034] S10, obtaining network download flow data of each application process of the detected application and a designated screen for determining whether the application is updated, and analyzing to obtain network download flow characteristics of the detected application and screen characteristics of the designated screen.

[0035] When users are using networked applications and the application is updated, the cloud server that deploys the application will inevitably download the update file from the public network to the local computer via the network card; therefore, during the operation of the application, monitoring the network download traffic of the application process can provide a reference for detecting application update behavior.

[0036] Optionally, a cloud server on which the detected application is deployed is set to periodically collect network download traffic data of the started application processes, and the collected network download traffic data is uploaded to an update server. The update server then analyzes the network download traffic data of each application process of the detected application to obtain the network download traffic characteristics of each application process.

[0037] Figure 3a is a schematic diagram of network download traffic of an application shown in an exemplary embodiment of the present disclosure; exemplarily, referring to Figure 3a As shown, in an embodiment of the present application, for example, the cloud server may obtain the network download traffic value of the monitored application process from the "Network" indicator item in the task manager of the operating system.

[0038] In another possible embodiment of the present application, the cloud server may also collect network download traffic data of the application process by calling an operating system API (Application Programming Interface).

[0039] In this embodiment, after setting up a cloud server to start the detected application, the cloud server collects video frame images of the application during its operation at a specified time, and sends the video frame images to the update server as a designated screen for determining whether the application has been updated. The update server can then determine whether the application has been updated by analyzing the features of the designated screen.

[0040] During the application update process, an update prompt screen will be generated, and since most application updates occur within 5 minutes after the application is started, in this embodiment, the cloud server is set to periodically collect video frames after the detected application is started, and the collected video frame images are used as the designated screen. Exemplarily, the application video frame collection is performed at the following different times: 1) 3 seconds after the application is started; 2) 10 seconds after the application is started; 3) 30*n seconds after the application is started, where n is an integer greater than or equal to 1 and less than or equal to 10; the specific collection time is not limited in this application.

[0041] Optionally, the cloud server obtains the network download traffic data of the application process within a preset time (such as within 5 minutes) after the application is started.

[0042] In another possible embodiment of the present application, the above-mentioned cloud server may also collect video frames when it receives external instructions such as notification from a manual detection system; and further, the data of network download traffic of each application process may also be collected when it receives external commands such as notification from a manual detection system; and further, in the present embodiment, application update detection may be triggered after receiving external instructions, and the specific triggering method is not limited in the present application.

[0043] The above-mentioned video frame acquisition method includes but is not limited to: acquiring the operating system desktop screen, acquiring the process window screen, or implementing application video acquisition at a specific time in the encoder module, etc.; this application does not limit this.

[0044] In an optional embodiment of the present application, in the above step S10, the network download traffic data of each application process of the detected application and the designated screen for determining whether the application has been updated are obtained, and the network download traffic characteristics of each application process and the screen characteristics of the designated screen are analyzed, specifically including the following steps A10-A20:

[0045] Step A10: Obtain network download traffic data of the application process during operation collected by each target cloud server that starts the application, calculate the growth rate of the network download traffic of each application process respectively, and obtain the network download traffic characteristics of each application process.

[0046] Exemplarily, after obtaining the network download traffic data of the application process collected by the target cloud server during its operation, the update server calculates the average network download traffic of the application process based on the network download traffic data, calculates the growth rate of the average network download traffic, and uses the growth rate of the average network download traffic of the application process of each target cloud server as the network download traffic feature of the application.

[0047] Step A20, and obtaining the designated screens of the application processes collected by each of the target servers, and performing feature extraction on the designated screens respectively to obtain screen features of the designated screens of each of the application processes.

[0048] For example, for the video frame uploaded by the target cloud server, the update server automatically recognizes the video frame in real time, and the recognition method includes: recognizing text information to obtain keywords; and / or recognizing specific pictures in the feature picture to obtain information about the specific picture, such as location, size, and iconic content. The above keywords and specific picture information are used as the picture features of the specified picture of the application.

[0049] Reference Figure 3bThe application update screen shown contains keywords such as "download", "update", and "confirm" that represent application updates, and an application update dialog box can be used as a specific picture.

[0050] For example, refer to Figure 3c The application update screen shown has an initialization progress bar for an update resource package representing the application update, and contains the keywords "initialization" and "resource package".

[0051] S20. If an application process triggers an automatic application update detection event, determine whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen.

[0052] Optionally, if the network download traffic characteristics of an application process or the screen characteristics of a specified screen hits a preset feature in a dedicated feature rule table stored in the feature library for calculating the probability of an update of the application, the application process is deemed to have triggered an automatic application update detection event, and the update server performs the application update detection process according to the above-mentioned update method.

[0053] In the embodiment of the present application, if there is a dedicated feature rule table corresponding to the application in the feature library, it means that the application has previously set a preset feature for judging the update of the application. In this case, if an application process of the application triggers an automatic application update detection event, then within the detection cycle, with reference to the situation where all application processes trigger the event, according to the number of application processes that trigger the automatic application update detection event within a complete detection cycle, the network download traffic characteristics of each application process that triggers the event, and the screen characteristics of the specified screen, the first probability adjustment rule corresponding to the preset feature in the dedicated feature rule table is used to calculate the first probability value characterizing the update of the application.

[0054] Exemplarily, the update server calculates the average network download traffic within a certain time period (configurable, and the configuration value can be automatically optimized according to the statistical value) for the network download traffic data of each application process obtained and reported in real time by the target cloud servers running the application, and calculates the growth rate of the average network download traffic. In addition, the update server performs feature recognition on the obtained designated screen of the application. If a special feature rule table for the application exists in the feature library, the probability value of the network download traffic feature is calculated according to the preset features in the special feature rule table using the number of application processes that trigger the automatic detection event within the detection period, the growth rate of the average network download traffic of each application process that triggers the event, and the screen features of the designated screen.

[0055] Optionally, in this embodiment, if an application process triggers an automatic application update detection event, the probability of the application being updated is calculated based on the network download traffic characteristics and screen characteristics of each application process of the application within the detection period and according to the application's dedicated feature rule table.

[0056] Exemplarily, the application-specific feature rule table is shown in Table 1 below.

[0057]

[0058] Table 1

[0059] Among them, the above-mentioned +a1 represents the probability value plus a1 (positive number), and the other a2, b1, b2, c2 are similar.

[0060] Exemplarily, referring to Table 1, the first probability adjustment rule is obtained based on empirical values, and the first probability adjustment rule may be different for different applications. And the preset features are manually configured.

[0061] In another optional embodiment of the present application, different weights may be set for the network download traffic feature and the picture feature in the above-mentioned dedicated feature rule table. For example, the weight of the picture feature is set greater than the weight of the network download traffic feature to further improve the accuracy of the final calculation result.

[0062] S30: If the application is updated, trigger the inspection and update tool to control the application to perform an update operation.

[0063] In this embodiment, after obtaining the first probability value, the first probability value is compared with the specified value. If the first probability value is greater than or equal to the specified value, it is determined that the application has been updated. In this case, the update server controls the application to directly perform the update operation, or controls the application to perform the update operation after reaching the specified time (such as after midnight).

[0064] The following is an example to illustrate that the process of determining whether an application is updated according to the features in the above-mentioned dedicated feature rule table may be:

[0065]

[0066]

[0067] Figure 4 is a schematic diagram of the interaction of various systems in a method for updating a cloud application shown in an exemplary embodiment of the present disclosure; Figure 4As shown, in the case where an automatic application update detection event is triggered, if the above-mentioned first probability value is less than a specified value, the update server 100 will continue to call the manual detection system 200 to detect application updates. Optionally, the update server 100 will obtain the network download traffic data, the specified screen, the network download traffic characteristics and the screen characteristics of the specified screen of the application process that triggered the automatic application update detection event and send them to the manual detection system 200 for reference and judgment by the staff. For example, the staff can restart an application process to check whether the application has been updated.

[0068] Exemplarily, in the above embodiment, the update server may also send the application's special feature rule table to the manual detection system, so that the reviewer can optimize the special feature rule table according to the manually updated detection results, and the manual detection system sends the optimized special feature rule table to the update server, and then the update server applies the optimized special feature rule table to replace the original special feature rule table of the application; in this embodiment, optimizing the application's special feature rule table by manual detection can further improve the accuracy of subsequent application update detection.

[0069] Refer again Figure 4 As shown, in the embodiment of the present application, after the update server 100 determines that the application is updated, before controlling the application to perform the update operation, it determines all cloud servers deployed by the application, and sets the application (not started) on all cloud servers to be in a locked state through the resource scheduling service 300. When the application is in a locked state, the application will not be started by the client. And with reference to the startup status of the application process on each cloud server, the cloud servers to be updated are divided into batches, and the application on the server with a lighter current load is updated first. The normal use of the cloud server can be guaranteed to be unaffected by updating in batches. Then, the update server 100 calls the inspection and update tool, and passes in the application identifier, the identifier list of the cloud server to be updated, and the application update operation script, so that the inspection and update tool refers to the application identifier and the cloud server identifier to execute the update operation script to update the application in batches. Exemplarily, the inspection and update tool can also be set on the update server.

[0070] In this embodiment, when an application is updated, the update server divides the cloud servers where the application is deployed and currently not started into batches according to the current startup status of the application, and then controls the inspection and update tool to update the applications on the cloud servers in batches.

[0071] After executing a batch of application updates on cloud servers, the inspection and update tool will generate an inspection report, which contains the results of the application updates. If there are some cloud servers whose application updates failed in the inspection execution report, the inspection and update tool will execute the update operation again for the applications on these cloud servers. If it fails again, the processing task generated based on the identification of the application that failed to update and the identification of the cloud server will be sent to the manual detection system for manual processing. After the final processing is successful, the application of the cloud server will be manually processed to end the locked state, or the special feature rules in the application update monitoring system will be manually corrected. For the application that has been successfully updated, the update server sets the state of the application on the cloud server to end the prohibition of terminal users from starting. After this state ends, the cloud server can provide cloud rendering services to the application normally.

[0072] In this embodiment, when batch processing application updates and upgrades, all servers that deploy the application will first be locked, so that the application is in a state that prohibits terminal users from starting it. Then, the application update is executed in batches, and the locked state of the application is ended after the application update is successful. It can be achieved that the application deployed on each cloud server is kept at the latest version and the version of the application on each cloud server is consistent, avoiding the following situation: user A uses version 1.0 of application C on cloud server B1. If application C on cloud server B1 triggers an update and is updated to version 1.1, after user A exits application C, when starting application C again, due to the uncertainty of cloud resource scheduling, it may be assigned to start application C on cloud server B2. If the version of application C on cloud server B2 is still version 1.0, user A will experience the version upgrade process of application C again, resulting in a very poor user experience. Therefore, the embodiment of the present application has a positive effect of improving user experience.

[0073] Optionally, the above-mentioned inspection and update tool has a desktop program version and a web version, which is triggered to start by passing in the following parameters: application identification, cloud server identification list, application parallel paths and update operation script; the inspection and update tool will automatically trigger the application startup and simulate the terminal user's automatic operation by automatically executing the update operation script, triggering the application to complete the update file download and the new version to take effect.

[0074] Figure 5 is a schematic diagram of the process of another method for updating a cloud application shown in an exemplary embodiment of the present disclosure; Figure 5 As shown, in the embodiment of the present application, the updating method of the cloud application further includes the following steps:

[0075] S201′, if an application process triggers a manual application update detection event, the network download traffic data, the designated screen, the network download traffic characteristics and the screen characteristics of the application process are sent to a manual detection system, and the application update detection operation is performed manually.

[0076] In this embodiment, if the network download traffic characteristics of an application process or the screen characteristics of a specified screen hits the general characteristics in the general feature rule table in the feature library for calculating the probability of any application being updated, this indicates that the feature library does not store a special feature rule table for characterizing the update of the application. In this case, the network download traffic data, the specified screen, the network download traffic characteristics and the screen characteristics of the application process are sent to the manual detection system, and the application update detection operation is performed manually.

[0077] After receiving the network download traffic data, designated screen, network download traffic features and screen features of the application process, the manual inspection system will automatically push the content to the auditor, including but not limited to: SMS, email, WeChat and other social software, etc. After receiving the pushed content, the auditor will perform the audit operation; optionally, the auditor can specify a cloud server to start the application through the web terminal, and can further manually verify whether the application update event has occurred.

[0078] S202 ′: Receive a detection result fed back by the manual detection system indicating whether the application has been updated.

[0079] After the manual detection is completed in this embodiment, the manual detection system feeds back the result to the update server, and the feedback result may also include a newly generated special feature rule table of the application. The update server receives and stores the special feature rule table of the application generated by the manual detection system. The special rule table stores the preset features that characterize the application update and the probability value adjustment rules corresponding to the preset features, which are used to calculate the probability of the application update. The purpose is to make the detection of subsequent version update events of the application more accurate and reduce the degree of manual intervention.

[0080] When the manual detection system detects an application update, it will generate an update operation script for the application through manual testing, and feed the update operation script back to the update server.

[0081] Exemplarily, the above-mentioned feedback results may include: setting a threshold range for the growth rate of the average network download traffic and the probability adjustment value of the feature, and providing a set of video frame keywords and their probability adjustment values, feature images and their probability adjustment values, etc. through auxiliary tools (such as screenshots, position box selection, etc.), and then the cloud server generates a special feature rule table for the application according to a predetermined template based on the feedback results.

[0082] In the technical solution provided by the present invention, the traffic characteristics of each application process and the characteristics of the characteristic screen are automatically inspected and analyzed in the system to effectively discover the hot update of the game, and the hot update is realized without the user's perception, which has the effects of accuracy, efficiency and cost saving.

[0083] Figure 6 is a schematic diagram of a server structure shown in an exemplary embodiment of the present disclosure; Figure 6 As shown, the server includes:

[0084] The monitoring module 401 is used to obtain network download flow data of each application process of the detected application and a designated screen for determining whether the application is updated, and analyze and obtain network download flow characteristics of each application process and screen characteristics of the designated screen;

[0085] The judgment module 402 is used to judge whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the specified screen if an application process triggers the automatic application update detection event;

[0086] The update module 403 is used to trigger the inspection update tool to control the application to perform an update operation if the application is updated.

[0087] Optionally, the above device further includes:

[0088] A sending module, for sending the network download flow data, the designated screen, the network download flow characteristics and the screen characteristics of the application process to a manual detection system if an application process triggers a manual application update detection event, so that the application update detection operation is manually performed;

[0089] The receiving module is used to receive the detection result fed back by the manual detection system, indicating whether the application has been updated.

[0090] Optionally, the above analysis module 401 is specifically used for:

[0091] Obtaining network download traffic data of the application process during operation collected by each target cloud server that starts the application, calculating the growth rate of the network download traffic of each application process respectively, and obtaining the network download traffic characteristics of each application process;

[0092] And obtaining the designated screens of the application processes collected by each of the target servers, respectively extracting features of the designated screens, and obtaining screen features of the designated screens of each of the application processes.

[0093] Optionally, the above-mentioned determination module 402 is specifically used for:

[0094] If the network download traffic feature of the application process or the screen feature of the specified screen hits the preset feature in the special feature rule table in the feature library for calculating the probability of the application being updated, then according to the number of application processes that trigger the automatic execution of the application update detection event within a detection cycle, the network download traffic feature of the application process and the screen feature of the specified screen, refer to the first probability adjustment rule of the corresponding preset feature in the special feature rule table to calculate a first probability value characterizing the update of the application;

[0095] The first probability value is compared with a specified value, and whether the application is updated is determined according to the comparison result.

[0096] Optionally, the sending module is specifically used to:

[0097] If the network download traffic characteristics of the application process or the screen characteristics of the specified screen hit the general characteristics in the general feature rule table in the feature library for calculating the probability of any application being updated, it means that the feature library does not store the special feature rule table for calculating the probability of the application being updated. In this case, the network download traffic data of the application process, the specified screen, the network download traffic characteristics and the screen characteristics are sent to the manual detection system.

[0098] Optionally, the sending module is further used to:

[0099] According to the number of application processes that trigger manual execution of application update detection events within a detection cycle, the network download traffic characteristics of the application processes, and the screen characteristics of the specified screen, a second probability adjustment rule corresponding to the preset characteristics in the general characteristic rule table is referred to to calculate a second probability value representing that the application is updated;

[0100] The second probability value, the network download flow data of the application process, the designated screen, the network download flow feature and the screen feature are sent to a manual detection system.

[0101] Optionally, the receiving module is further used for:

[0102] Receive the special feature rule table for calculating the probability of the application being updated generated and fed back by the manual detection system, the special rule table storing preset features characterizing the application being updated and probability value adjustment rules corresponding to the preset features, the probability value adjustment rules being used to calculate the probability of the application being updated, and store the special feature rule table in a feature library.

[0103] Optionally, the device further comprises:

[0104] A search module, used to search for all cloud servers where the application is deployed, and set the unstarted application deployed on each cloud server to a state where the terminal user is prohibited from starting it;

[0105] The determination module is used to determine the identification list of cloud servers for performing the application update in batches according to the load conditions of the cloud servers.

[0106] Optionally, the updating module 403 is specifically used to:

[0107] Calling the inspection and update tool, passing in the update operation script of the application, the identifier of the application and the identifier list of the cloud server to be updated, so that the inspection tool applies the update operation script of the application according to the identifier list of the cloud server to control the application to be updated;

[0108] After the application is successfully updated, the application is set to stop being in a state where the terminal user is prohibited from starting the application.

[0109] In another embodiment of the present disclosure, a machine-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned cloud application update method are implemented to timely discover application updates and control the application to automatically update, thereby improving efficiency.

[0110] Figure 7 Schematic diagram of the structure of an electronic device shown in the embodiment of the present disclosure. Figure 7 As shown, the electronic device 500 at least includes a memory (machine-readable storage medium) 502 and a processor 501; the memory 502 is connected to the processor 501 via a communication bus 503, and is used to store instruction codes executable by the processor 501; the processor 501 is used to read and execute instruction codes from the memory 502 to implement the steps of the cloud application update method described in any of the above embodiments. In order to realize timely discovery of application updates, control the application to automatically update, and improve efficiency.

[0111] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0112] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0113] Computers suitable for executing computer programs include, for example, general and / or special microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the computer will also include one or more large-capacity storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to this large-capacity storage device to receive data from it or to transmit data to it, or both. However, the computer does not necessarily have such a device. In addition, the computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0114] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0115] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of the specific embodiments of specific inventions. Certain features described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claim protection, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of a sub-combination.

[0116] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or requiring that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0117] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0118] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A method for updating a cloud application, characterized in that: The method comprises: Obtain network download traffic data of each application process of the detected application and a designated screen for determining whether the application has been updated, and analyze and obtain network download traffic characteristics of each application process and screen characteristics of the designated screen; If an application process triggers an automatic application update detection event, determining whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen; If the application is updated, the inspection and update tool is triggered to control the application to perform an update operation; Wherein, if an application process triggers an automatic application update detection event, judging whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen, includes: If the network download traffic feature of the application process or the screen feature of the specified screen hits the preset feature in the special feature rule table in the feature library for calculating the probability of the application being updated, then according to the number of application processes that trigger the automatic execution of the application update detection event within a detection cycle, the network download traffic feature of the application process and the screen feature of the specified screen, refer to the first probability adjustment rule of the corresponding preset feature in the special feature rule table to calculate a first probability value characterizing the update of the application; The first probability value is compared with a specified value, and whether the application is updated is determined according to the comparison result.

2. The method according to claim 1, characterized in that: The method further comprises: If an application process triggers a manual application update detection event, the network download flow data, the designated screen, the network download flow characteristics and the screen characteristics of the application process are sent to a manual detection system, and the application update detection operation is performed manually; A detection result indicating whether the application is updated is received from the manual detection system.

3. The method according to claim 1 or 2, characterized in that: The acquiring of network download flow data of each application process of the detected application and a designated screen for determining whether the application is updated, and analyzing to obtain network download flow characteristics of each application process and screen characteristics of the designated screen, includes: Obtain network download traffic data of the application process collected by each target cloud server that starts the application during operation, calculate the growth rate of the network download traffic of each application process respectively, and obtain the network download traffic characteristics of each application process; And obtaining the designated screens of the application processes collected by each target server, respectively extracting features from the designated screens, and obtaining screen features of the designated screens of each application process.

4. The method according to claim 2, characterized in that: If an application process triggers a manual application update detection event, sending the network download flow data, the designated screen, the network download flow characteristics and the screen characteristics of the application process to the manual detection system includes: If the network download traffic characteristics of the application process or the screen characteristics of the specified screen hit the general characteristics in the general feature rule table in the feature library for calculating the probability of any application being updated, it means that the feature library does not store the special feature rule table for calculating the probability of the application being updated. In this case, the network download traffic data of the application process, the specified screen, the network download traffic characteristics and the screen characteristics are sent to the manual detection system.

5. The method according to claim 4, characterized in that Before sending the network download flow data of the application process, the designated screen, the network download flow characteristics and the screen characteristics to the manual detection system, the method further includes: According to the number of application processes that trigger manual execution of application update detection events within a detection cycle, the network download traffic characteristics of the application processes, and the screen characteristics of the specified screen, a second probability adjustment rule corresponding to the preset characteristics in the general characteristic rule table is referred to to calculate a second probability value representing that the application is updated; The sending the network download flow data of the application process, the designated screen, the network download flow characteristics and the screen characteristics to the manual detection system includes: The second probability value, the network download flow data of the application process, the designated screen, the network download flow feature and the screen feature are sent to a manual detection system.

6. The method according to claim 4, characterized in that After sending the network download flow data of the application process, the designated screen, the network download flow characteristics and the screen characteristics to the manual detection system, the method further includes: Receive the special feature rule table generated and fed back by the manual detection system for calculating the probability of the application being updated, and store the special feature rule table in a feature library; wherein the special feature rule table stores preset features characterizing the application being updated and probability value adjustment rules corresponding to the preset features, and the probability value adjustment rules are used to calculate the probability of the application being updated.

7. The method according to claim 1, characterized in that Before the trigger inspection and update tool controls the application to perform an update operation, the method includes: Find all cloud servers where the application is deployed, and set the unstarted application deployed on each cloud server to a state where a terminal user is prohibited from starting it; According to the load conditions of the cloud servers, a list of identifiers of the batches of cloud servers for performing the application update in batches is determined.

8. The method according to claim 7, characterized in that The trigger inspection update tool controls the application to perform an update operation, including: Obtain an update operation script for the application, the application identifier and a list of identifiers of cloud servers on which the application is to be updated, and pass the update operation script for the application, the application identifier and a list of identifiers of cloud servers on which the application is to be updated to the inspection and update tool, so that the inspection and update tool uses the update operation script for the application according to the list of identifiers of the cloud servers to sequentially control the update of the application on each cloud server in the list.

9. A server, characterized in that: The server comprises: An analysis module, used to obtain network download flow data of each application process of the detected application and a designated screen for determining when the application is updated, and to analyze and obtain network download flow characteristics of each application process and screen characteristics of the designated screen; A judgment module, configured to judge whether the application is updated based on the number of application processes that trigger the automatic application update detection event within the same detection cycle, the network download traffic characteristics of the application process, and the screen characteristics of the designated screen if an application process triggers the automatic application update detection event; An update module, configured to trigger the inspection and update tool to control the application to perform an update operation if the application is updated; Among them, the judgment module is specifically used to calculate a first probability value characterizing the update of the application based on the number of application processes that trigger the automatic execution of application update detection events within a detection cycle, the network download traffic characteristics of the application process and the screen characteristics of the specified screen, and refer to the first probability adjustment rule corresponding to the preset characteristics in the special feature rule table if the network download traffic characteristics of the application process or the screen characteristics of the specified screen hits the preset characteristics in the special feature rule table in the feature library for calculating the probability of the application being updated; compare the first probability value with the specified value, and judge whether the application is updated based on the comparison result.

10. A machine-readable storage medium, characterized in that: A plurality of computer instructions are stored on the machine-readable storage medium, and when the computer instructions are executed, the method according to any one of claims 1 to 8 is performed.

11. An electronic device, characterized in that: include: A machine-readable storage medium and a processor, wherein the machine-readable storage medium: stores instruction codes; Processor: communicates with the machine-readable storage medium, reads and executes the instruction code in the machine-readable storage medium, and implements the method according to any one of claims 1-8.

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