Application management method and device, equipment and storage medium

Abstract

The embodiment of the invention provides an application management method and device, equipment and a storage medium. The method comprises the steps that in the running process of the cloud application, performance information of service equipment used for running the cloud application is determined, and the performance information indicates the use state of computing resources of the service equipment; determining a target data processing amount of a text recognition module associated with the cloud application based on the performance information; according to the target data processing amount, a plurality of image frames of the cloud application are provided for a text recognition module to determine a text recognition result of the plurality of image frames, and the text recognition result reflects whether text content included in the plurality of image frames meets a preset constraint or not; and managing the operation of the cloud application based on the text recognition result. The processing frequency of the text recognition module is dynamically adjusted on the basis of the performance information of the service equipment running the cloud application, occupation of computing resources by the service equipment during text recognition is reduced, self-adaptive control over power consumption is achieved, and the management efficiency of the cloud application is improved.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and more particularly to methods, apparatus, devices, and computer-readable storage media for application management. Background Technology

[0002] With the rapid development of information technology, cloud computing has become a core component of modern enterprise information technology architecture. Cloud applications leverage the powerful capabilities of cloud computing, allowing users to access data stored on remote service devices via the network. How to manage cloud applications is a key concern. Summary of the Invention

[0003] In a first aspect of this disclosure, an application management method is provided. The method includes: during the operation of a cloud application, determining performance information of a service device used to run the cloud application, the performance information indicating the usage status of the computing resources of the service device; based on the performance information, determining a target data processing volume for a text recognition module associated with the cloud application; according to the target data processing volume, providing the text recognition module with multiple image frames of the cloud application to determine text recognition results for the multiple image frames, the text recognition results reflecting whether the text content included in the multiple image frames meets preset constraints; and managing the operation of the cloud application based on the text recognition results.

[0004] In a second aspect of this disclosure, an apparatus for application management is provided. The apparatus includes: a first determining module configured to determine performance information of a service device used to run the cloud application during the operation of the cloud application, the performance information indicating the usage status of the computing resources of the service device; a second determining module configured to determine a target data processing volume for a text recognition module associated with the cloud application based on the performance information; a providing module configured to provide multiple image frames of the cloud application to the text recognition module according to the target data processing volume, to determine text recognition results of the multiple image frames, the text recognition results reflecting whether the text content included in the multiple image frames meets preset constraints; and a management module configured to manage the operation of the cloud application based on the text recognition results.

[0005] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0007] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0008] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0009] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0010] Figure 2 A flowchart illustrating an application management process according to some embodiments of this disclosure is shown;

[0011] Figure 3 Example diagrams of an application management system according to some embodiments of the present disclosure are shown;

[0012] Figure 4 A schematic structural block diagram of an apparatus for application management according to certain embodiments of the present disclosure is shown;

[0013] Figure 5 A block diagram of an electronic device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0016] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0017] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0018] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information other than that necessary for basic functions will not affect the user's use of basic functions.

[0019] Traditionally, when using text recognition modules for text recognition in cloud applications, the high computational resource consumption is a common problem. Because the text recognition algorithms used by these modules handle image data complexly, they require significant computing resources, leading to performance degradation and even lag in service devices. Furthermore, the fixed processing frequency of text recognition cannot meet the needs of different cloud application scenarios, resulting in resource waste and unstable system response speed.

[0020] In addition, related technologies can also perform text recognition based on non-local text recognition modules. However, this recognition method requires additional links and additional image information interaction, which reduces real-time performance and increases costs in all aspects.

[0021] This disclosure proposes an application management scheme. According to this scheme, during the operation of a cloud application, performance information of the service device used to run the cloud application is determined, indicating the usage status of the service device's computing resources; based on the performance information, the target data processing volume of the text recognition module associated with the cloud application is determined; according to the target data processing volume, multiple image frames of the cloud application are provided to the text recognition module to determine the text recognition results of the multiple image frames, the text recognition results reflecting whether the text content included in the multiple image frames meets preset constraints; and based on the text recognition results, the operation of the cloud application is managed.

[0022] Based on this approach, the embodiments of this disclosure can dynamically adjust the processing frequency of the text recognition module based on the performance information of the service device running the cloud application, thereby reducing the computational resource consumption of the service device when performing text recognition, achieving adaptive power consumption control, and improving the management efficiency of the cloud application.

[0023] Example Environment

[0024] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, example environment 100 may include service device 110 and terminal device 120.

[0025] In this example environment 100, service device 110 can run cloud application 130. Cloud application 130 can be any suitable application, such as a game application, video application, rendering application, etc.

[0026] User 140 can access the cloud application 130 running on the service device 110 through the terminal device 120, and thus experience the services provided by the cloud application 130. In some embodiments, by deploying the cloud application on the service device, the user 140 can be spared the operations of downloading, installing, and upgrading, ensuring that the user 140 can experience the services provided by the cloud application 130 anytime, anywhere on multiple platforms via the network, and enjoy high-quality services based on simple hardware such as terminals and set-top boxes.

[0027] Taking cloud application 130 as a game application as an example, the service device can render the game's visuals and scenes into a video stream and transmit it to the terminal device 120 for display in real time via the network. Additionally, the user can input a target instruction on the terminal device 120, allowing the service device to interact with the user in the game based on this instruction.

[0028] To ensure the security of cloud applications during operation, service devices can dynamically capture effective information from the interactive interface of cloud applications based on Optical Character Recognition (OCR) and use it for security monitoring, passing it to security monitoring components or business middleware to achieve effective monitoring of cloud applications. OCR technology is a technology that converts printed or handwritten text into machine-readable text. It mainly involves recognizing and extracting characters from images or documents and then converting them into encoded text that can be processed by computers.

[0029] In some embodiments, terminal device 120 communicates with service device 110 to provide services to application 130. Terminal device 120 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, handheld computers, portable gaming terminals, VR / AR devices, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 120 can also support any type of interface for the target user (such as "wearable" circuitry).

[0030] Service device 110 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. Service device 110 may include, for example, computing systems / servers such as mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0031] A communication connection can be established between the service device 110 and the terminal device 120. This communication connection can be established via wired or wireless means. The communication connection may include, but is not limited to, Bluetooth, mobile network, Universal Serial Bus (USB), and Wireless Fidelity (WiFi) connections, and the embodiments of this disclosure are not limited in this respect. In the embodiments of this disclosure, the service device 110 and the terminal device 120 can perform signaling interaction through the communication connection between them.

[0032] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0033] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure.

[0034] Example process

[0035] Figure 2 A flowchart of an application management process 200 according to some embodiments of the present disclosure is shown. Process 200 can be implemented at service device 110. Reference is made below. Figure 1 Describe the process 200.

[0036] In box 210, during the operation of cloud application 130, service device 110 determines the performance information of service device 110 used to run cloud application 130. The performance information indicates the usage status of computing resources of service device 110.

[0037] In some embodiments, the service device 110 runs a cloud application 130, which can be any suitable application, such as a game application, a video application, a cloud rendering application, or other applications that can generate images, which will not be elaborated here.

[0038] In some embodiments, computing resources can be any suitable resources, which may include, but are not limited to, processing resources of the service device 110 and storage resources of the service device 110. Processing resources may include a central processing unit (CPU), network bandwidth, input / output capabilities, etc. Storage resources may include internal storage, external storage, etc.

[0039] In some embodiments, the usage state can be any appropriate state, such as utilization rate or idle value, etc.

[0040] As an example, performance information may include, but is not limited to, the CPU idle value of service device 110, the storage space of service device 110, and so on.

[0041] In some embodiments, the cloud application 130 may correspond to different operating stages during operation, such as the initial stage of the cloud application 130, the usage stage of the cloud application 130, the exit stage of the cloud application 130, etc.

[0042] The initial stage of cloud application 130 is the stage entered after being requested or triggered for startup. When cloud application 130 is in the initial stage, it can load necessary resources, such as code repositories, configuration files, and dependencies, and can also initialize database connections, set logging, and configure security parameters. Taking a game application as an example, the initial stage can be the stage entered by user 140 after launching the game application based on terminal device 120. At this time, cloud application 130 can load necessary resources, such as the game engine, graphics resources, audio files, and initial scene data.

[0043] The usage phase of cloud application 130 is the phase that begins after the initial phase. At this time, all services and functions of cloud application 130 are activated, and it can receive and process input from user 140, execute corresponding business logic, and return results to user 140. Taking cloud application 130 as a game application as an example, the usage phase is the phase where all game resources and systems have been loaded, and user 140 can start the game to explore, complete tasks, and interact.

[0044] The exit phase of cloud application 130 is the phase that cloud application 130 enters when it needs to stop service. In this phase, cloud application 130 can stop accepting new requests from user 140 and begin to orderly close ongoing tasks and connections. Taking cloud application 130 as a game application as an example, the exit phase is the phase that user 140 enters when choosing to exit the game or when the game needs to be closed. At this time, the game application will stop accepting input from user 140.

[0045] In box 220, service device 110 determines the target data processing volume of the text recognition module associated with cloud application 130 based on performance information.

[0046] In some embodiments, the target data processing volume can be any appropriate data processing volume, such as throughput, data throughput rate, latency, response time, etc.

[0047] In some embodiments, the service device 110 may determine the target data processing volume based on the idle value of the central processing unit. Taking throughput as an example, the larger the idle value of the central processing unit, the larger the target data processing volume.

[0048] In other embodiments, the service device 110 may determine the target data processing volume based on its storage space. Taking throughput as an example, the larger the storage space, the larger the target data processing volume.

[0049] In other embodiments, service device 110 may determine a target state of cloud application 130, which indicates the operational phase of cloud application 130. In some embodiments, the target state may include, but is not limited to, an initial state, a usage state, and an exit state. The initial state is the state of service device 110 in the initial phase, the usage state is the state of service device 110 in the usage phase, and the exit state is the state of service device 110 in the exit phase.

[0050] In some embodiments, the service device 110 may determine a first data processing volume based on a target state.

[0051] In some embodiments, when the target state of cloud application 130 is the initial state or the exit state, the rendering frame rate of cloud application 130 is not high. For example, in cloud gaming scenarios, the interfaces with low frame rates for game applications are often at the beginning of the game, such as login screens and progress bar loading screens.

[0052] In some embodiments, the service device 110 may determine a first data processing amount based on a predetermined minimum data processing amount in response to the target state being an initial state or an exit state. The predetermined minimum data processing amount can be set as needed.

[0053] As an example, if the target state is an initial startup state or an exit state, the service device 110 can determine the first data processing volume based on the following formula:

[0054] T S =min(T), S = 0 or S = 2;

[0055] Among them, T S S represents the first data processing volume; S represents the target state of cloud application 130, S=0 represents the target state as the initial state, S=2 represents the target state as the exit state; min(T) represents the predetermined minimum data processing volume.

[0056] In other embodiments, the service device 110 may determine a first data processing volume based on the rendering frame rate corresponding to the cloud application 130 in response to the target state being in use. The rendering frame rate is the rate at which the service device 110 generates image data and transmits it to the client terminal in a cloud gaming or cloud rendering application.

[0057] In some embodiments, when the target state of cloud application 130 is in use, the rendering frame rate is in the rising phase.

[0058] In some embodiments, when the rendering frame rate is less than a threshold, the first data processing volume can increase in a second trend as the rendering frame rate increases, in order to meet the needs of the rapid changes in the game screen and thus capture the target text information.

[0059] In some embodiments, when the rendering frame rate is greater than or equal to a threshold, the first data processing volume can exhibit a first increasing trend as the rendering frame rate increases. That is, when the rendering frame rate reaches a relatively high critical point, the scene change information corresponding to the target screen of cloud application 130 is relatively small, meaning the redundancy of information in the target screen is high. Therefore, the growth trend of the first data processing volume can be set to decrease, i.e., the second increasing trend is lower than the first increasing trend.

[0060] As an example, the first growth trend can correspond to exponential growth, and the second growth trend can correspond to logarithmic growth.

[0061] As an example, if the target state is a usage state, the service device 110 can determine the first data processing volume based on the following formula:

[0062] T f =a·e b·f ,iff <F

[0063] T f =c·log(d·(f+n))+m,iff≥F

[0064] T S =T f S = 1

[0065] Among them, T S S represents the first data processing volume; S represents the target state of cloud application 130, where S=1 indicates that the target state is the usage state. f represents the rendering frame rate corresponding to cloud application 130; a, b, c, d, and m are predetermined parameter values ​​that can be set according to requirements; F represents the threshold, which represents the threshold point at which the acceleration of the data processing volume decreases as the frame rate increases, and can be dynamically configured according to requirements in actual scenarios.

[0066] If the data processing volume T is defined as the number of frames processed within 10 seconds, when a = 0.8, b = 0.2, and f = 30, T f The value is close to 300 frames. When c=150, d=6, n=-22, m=50, and f=30, T f The value is close to 300 frames.

[0067] In some embodiments, the service device 110 may determine the second data processing volume based on the first data processing volume and performance information.

[0068] As an example, service device 110 can determine the second data processing volume based on the following formula:

[0069]

[0070] Among them, T c T represents the second data processing volume; S The first data processing volume is represented by c; c represents the idle value of the central processing unit of the service device 110; g is a predetermined parameter value that represents the rate at which the data processing volume decreases when c is low, and it can be set according to requirements.

[0071] g decides T c As an example, the rate of decrease is approximately 10% when g = 10 and c = 25.

[0072] In some embodiments, the service device 110 may determine the target data processing volume based on a second data processing volume, a predetermined minimum data processing volume, and a predetermined maximum data processing volume. The predetermined maximum data processing volume is the maximum limit of the current system frame rate, and the predetermined minimum data processing volume is the minimum limit of the current system frame rate. The predetermined maximum data processing volume and the predetermined minimum data processing volume can be set as needed.

[0073] Furthermore, the service device 110 can determine a candidate data processing volume based on the minimum of the second data processing volume and the predetermined maximum data processing volume. That is, the service device 110 can determine the minimum of the first data processing volume and the predetermined maximum data processing volume, and set this minimum value as the candidate data processing volume. The service device 110 can determine a target data processing volume based on the maximum of the candidate data processing volume and the minimum data processing volume. That is, the service device 110 can determine the maximum of the candidate data processing volume and the minimum data processing volume, and set this maximum value as the target data processing volume.

[0074] As an example, service device 110 can determine the target data processing volume based on the following formula:

[0075] T = max(min(T), min(max(T), T) c ))

[0076] Where T represents the target data processing volume; min(T) represents the predetermined minimum data processing volume, and max(T) represents the predetermined maximum data processing volume.

[0077] In frame 230, the service device 110 provides multiple image frames from the cloud application 130 to the text recognition module according to the target data processing volume, so as to determine the text recognition results of the multiple image frames. The text recognition results reflect whether the text content included in the multiple image frames meets the preset constraints.

[0078] In some embodiments, the text recognition module can determine the text recognition results corresponding to multiple image frames based on a predetermined recognition algorithm. The predetermined recognition algorithm can be any suitable algorithm, such as an OCR algorithm, or any other suitable algorithm, etc., which will not be elaborated here.

[0079] In some embodiments, the text recognition result can reflect whether the text content included in multiple image frames meets preset constraints, which can be any appropriate constraint. In some embodiments, the preset constraints can indicate whether the multiple image frames include text of a predetermined type. The predetermined type can be any appropriate type, such as a sensitive type.

[0080] In other embodiments, a preset constraint may indicate whether a plurality of image frames include text that appears more than a predetermined threshold. The predetermined threshold can be set as needed. As an example, the preset constraint may indicate text that appears more than once in a plurality of image frames, i.e., the union of all text appearing in the plurality of image frames (all text appearing in the plurality of image frames). As another example, the preset constraint may indicate text that appears in every single image frame in the plurality of image frames, in which case the predetermined threshold is equal to the number of the plurality of image frames; i.e., the preset constraint may indicate the intersection of all text appearing in the plurality of image frames (common text appearing in the plurality of image frames).

[0081] In other embodiments, preset constraints may indicate whether multiple image frames include text of a predetermined type and whether multiple image frames include text that appears more than a predetermined threshold number of times.

[0082] In some embodiments, different recognition algorithms can be used for text recognition results that reflect different content. For example, for text recognition results that reflect whether the text content included in multiple image frames contains sensitive text, recognition algorithm A can be used, while for text recognition results that reflect common text included in multiple image frames, recognition algorithm B can be used.

[0083] The following explains how to determine which recognition algorithm (target recognition algorithm) to use to determine the text recognition result.

[0084] In some embodiments, the service device may include a service configuration module for configuring various algorithms that can be used for text recognition. The service configuration module can receive a management dimension corresponding to a cloud application input by the user. Based on this management dimension, the service configuration module can determine the target recognition algorithm corresponding to that management dimension. The text recognition module can obtain this target recognition algorithm sent by the service configuration module to determine the text recognition result based on that algorithm. The management dimension can be any suitable dimension, and the recognition algorithm corresponding to each management dimension may differ. Furthermore, the preset constraints differ for different management dimensions.

[0085] As an example, if we use applications as the management dimension, then different applications will have different recognition algorithms. For instance, the recognition algorithm for application A will be different from that for application B. As another example, if we use applications and the department / company managing those applications as the management dimensions, then the management dimensions will differ depending on whether the same department manages different applications, the same application manages different departments / company, and different applications manage different departments / company. For example, the recognition algorithm for department C managing application A will be different from that for department B managing application A.

[0086] In some embodiments, multiple image frames of cloud application 130 are image frames generated by service device 110 based on a request input by user 140.

[0087] In some embodiments, the larger the target data processing volume, the more image frames of the cloud application 130 are provided to the text recognition module.

[0088] This embodiment of the disclosure can control the target data processing volume under different target states, thereby controlling the power consumption of the text recognition module based on the target data processing volume. Simultaneously, it operates with low power consumption when the cloud application is in its initial or exit state, without the user's awareness. While the cloud application 130 is in use, it adaptively adjusts the image frames provided to the text recognition module, making the power consumption switching process imperceptible and the effect stable.

[0089] In frame 240, service device 110 manages the operation of cloud application 130 based on text recognition results.

[0090] In some embodiments, the service device 110 may perform management of a predetermined type based on the text recognition results, such as monitoring management and management of any other appropriate functions, wherein monitoring management may be used to detect whether the multiple image frames contain sensitive information, determine the accuracy of the generated image frames, and so on.

[0091] Figure 3Example diagrams of an application management system according to some embodiments of the present disclosure are shown, now for... Figure 3 Please provide an explanation.

[0092] by Figure 3 As an example, the application management system mainly includes: power consumption control module 301, state machine module 302, control configuration module 303 (including scheduling instruction control module 303-1, business configuration module 303-2, etc.), algorithm module 304 (including image data acquisition module 304-1, OCR algorithm module 304-2, text processor module 304-3), and event execution and state switching module 305.

[0093] In some embodiments, the scheduling instruction control module 303-1 is used to interact with the cloud application scheduling side (also known as the scheduling system, such as a PaaS scheduling system). It can control the behavior of the current session based on various parameters configured by the cloud application scheduling side. The session is associated with user 140. Various parameters may include parameters corresponding to terminal device 120 and service device 110, etc.

[0094] The state machine module 302 is used to determine the target state corresponding to the cloud application 130. The target state may include the initial state, the usage state, and the exit state.

[0095] The power consumption control module 301 controls the target data processing volume obtained from the image data acquisition module 304-1 to the OCR algorithm module 304-2 based on the combined results of the target state of the cloud application 130 determined by the state machine module 302, system performance monitoring, and frame rate monitoring. System performance monitoring primarily monitors the performance information corresponding to the service device 110, which can indicate the idle value of the central processing unit of the service device 110, the storage space of the service device 110, etc. Frame rate monitoring primarily monitors the rendering frame rate corresponding to the cloud application 130. Rendering frame rate, performance information, etc., are all example parameters for determining the target data processing volume.

[0096] The business configuration module 303-2 is used to control the configuration of functions within the container under various business scenarios. Specifically, the business configuration module configures all available recognition algorithms for the service device 110 when performing text recognition. In some embodiments, the business configuration module 303-2 is specifically used to configure the specific content to be recognized and the behavior strategy after the recognized content is determined during the OCR recognition process of the cloud application 130. Specifically, the business configuration module 303-2 can receive a user-input management dimension and determine the target recognition algorithm corresponding to this management dimension from among the available recognition algorithms. The business configuration module 303-2 can then send the target recognition algorithm to the OCR algorithm module 304-2.

[0097] The OCR algorithm module 304-2 can be used to determine the text recognition result corresponding to the image frames acquired from the image data acquisition module 304-1. The OCR algorithm module 304-2 can also receive target recognition algorithms sent by the service configuration module 303-2; that is, the OCR algorithm module 304-2 can determine which image frames to recognize and which recognition algorithm to use to recognize these image frames, thereby obtaining the text recognition result. The OCR algorithm module 304-2 can package the recognized text recognition result into a result data packet. The result data packet may contain text information included in the image frame, text coordinates, confidence scores, and other related information.

[0098] The text processor module 304-3 analyzes the result data packets sent by the OCR algorithm module to determine the behavior strategy. In some embodiments, the text processor module 304-3 can determine whether the processing result is a hit based on the text data packets. As an example, the text processor module 304-3 can determine that the processing result is a hit when it is determined that the text content included in the image frame meets predetermined constraints.

[0099] The event execution and state switching module 305 is used to execute actions corresponding to the behavior strategy based on the behavior strategy. These actions may include, but are not limited to, event specification, state switching, and result reporting. In some embodiments, the event execution and state switching module 305 may, in response to determining that the processing result is a hit, perform at least one of the following: event specification, state switching, or result reporting. In some embodiments, the event execution and state switching module 305 may, in response to determining that the processing result is a miss, wait for data callback.

[0100] Based on this approach, the embodiments of this disclosure can dynamically adjust the processing frequency of the text recognition module based on the performance information of the service device 110 running the cloud application 130, thereby reducing the computational resource consumption of the service device 110 when performing text recognition, achieving adaptive power consumption control, and improving the management efficiency of the cloud application 130.

[0101] Example devices and equipment

[0102] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 4 A schematic structural block diagram of an application management apparatus 400 according to certain embodiments of the present disclosure is shown. Apparatus 400 may be implemented as or included in the service device 110 discussed above. Various modules / components in apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0103] like Figure 4As shown, the device 400 includes a first determining module 410, configured to determine performance information of the service device used to run the cloud application during the operation of the cloud application, wherein the performance information indicates the usage status of the computing resources of the service device; a second determining module 420, configured to determine the target data processing volume of the text recognition module associated with the cloud application based on the performance information; a providing module 430, configured to provide multiple image frames of the cloud application to the text recognition module according to the target data processing volume, so as to determine the text recognition results of the multiple image frames, wherein the text recognition results reflect whether the text content included in the multiple image frames meets preset constraints; and a management module 440, configured to manage the operation of the cloud application based on the text recognition results.

[0104] In some embodiments, the second determining module 420 is further configured to: determine a target state of the cloud application, the target state indicating the operational stage of the cloud application; determine a first data processing volume based on the target state; determine a second data processing volume based on the first data processing volume and performance information; and determine a target data processing volume based on the second data processing volume, a predetermined minimum data processing volume, and a predetermined maximum data processing volume.

[0105] In some embodiments, the target state includes the usage state of the cloud application, and the second determining module 420 is further configured to: in response to the target state being the usage state, determine a first data processing amount based on the rendering frame rate corresponding to the cloud application.

[0106] In some embodiments, in response to a drawing frame rate greater than or equal to a threshold, the first data processing volume exhibits a first increasing trend as the drawing frame rate increases; and in response to a drawing frame rate less than the threshold, the first data processing volume exhibits a second increasing trend as the drawing frame rate increases, wherein the first increasing trend is less than the second increasing trend.

[0107] In some embodiments, the first growth trend corresponds to exponential growth, and the second growth trend corresponds to logarithmic growth.

[0108] In some embodiments, the target state includes an initial state or an exit state based on the target state, and the second determining module 420 is further configured to: determine a first data processing amount based on a predetermined minimum data processing amount in response to the target state being an initial state or an exit state.

[0109] In some embodiments, computing resources include at least one of the following:

[0110] Processing resources of service equipment

[0111] Storage resources of service equipment.

[0112] In some embodiments, the second determining module 420 is further configured to: determine a candidate data processing volume based on the minimum of the second data processing volume and the predetermined maximum data processing volume; and determine a target data processing volume based on the maximum of the candidate data processing volume and the minimum data processing volume.

[0113] In some embodiments, the preset constraint indicates at least one of the following: whether the plurality of image frames include text of a predetermined type, and whether the plurality of image frames include text that appears more than a predetermined threshold number of times.

[0114] The units included in device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units may be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the units in device 400 may be implemented at least partially by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), and so on.

[0115] Figure 5 A block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 5 The electronic device 500 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown can be used to achieve Figure 1 The service equipment 110 shown.

[0116] like Figure 5 As shown, electronic device 500 is in the form of a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors or processing units 510, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processing unit 510 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 500.

[0117] Electronic device 500 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 520 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 530 can be a removable or non-removable medium and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within electronic device 500.

[0118] Electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 5 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 520 may include computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0119] Communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 500 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0120] Input device 550 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 560 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 500 can also communicate with one or more external devices (not shown) via communication unit 540 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 500, or with any device that enables electronic device 500 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0121] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0122] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0123] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0124] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0126] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. An application management method, comprising: During the operation of a cloud application, performance information of the service device used to run the cloud application is determined, and the performance information indicates the usage status of the computing resources of the service device. Based on the performance information, determine the target data processing volume of the text recognition module associated with the cloud application; Based on the target data processing volume, multiple image frames of the cloud application are provided to the text recognition module to determine the text recognition results of the multiple image frames. The text recognition results reflect whether the text content included in the multiple image frames meets preset constraints. as well as Based on the text recognition results, the operation of the cloud application is managed.

2. The method according to claim 1, wherein determining the target data processing volume of the text recognition module associated with the cloud application based on the performance information includes: Determine the target state of the cloud application, wherein the target state indicates the operational stage of the cloud application; Based on the target state, determine the first data processing volume; Based on the first data processing volume and the performance information, the second data processing volume is determined; as well as The target data processing volume is determined based on the second data processing volume, the predetermined minimum data processing volume, and the predetermined maximum data processing volume.

3. The method according to claim 2, wherein the target state includes the usage state of the cloud application, and determining the first data processing volume based on the target state includes: In response to the target state being the usage state, the first data processing volume is determined based on the rendering frame rate corresponding to the cloud application.

4. The method according to claim 3, wherein: In response to the rendering frame rate being greater than or equal to a threshold, the first data processing volume exhibits a first increasing trend as the rendering frame rate increases; and In response to the rendering frame rate being less than the threshold, the first data processing volume exhibits a second growth trend as the rendering frame rate increases, and the first growth trend is less than the second growth trend.

5. The method of claim 4, wherein the first growth trend corresponds to exponential growth and the second growth trend corresponds to logarithmic growth.

6. The method of claim 2, wherein the target state includes an initial state or an exit state, and determining the first data processing volume based on the target state includes: In response to the target state being the initial state or the exit state, the first data processing volume is determined based on the predetermined minimum data processing volume.

7. The method of claim 1, wherein the computing resources include at least one of the following: The processing resources of the service equipment. The storage resources of the service device.

8. The method of claim 2, wherein determining the target data processing volume based on the second data processing volume, the predetermined minimum data processing volume, and the predetermined maximum data processing volume comprises: Based on the minimum value between the second data processing volume and the predetermined maximum data processing volume, a candidate data processing volume is determined; as well as The target data processing volume is determined based on the maximum value among the candidate data processing volumes and the minimum data processing volume.

9. The method of claim 1, wherein the preset constraint indicates at least one of the following: Whether the plurality of image frames include text of a predetermined type. Does the plurality of image frames include text that appears more than a predetermined threshold number of times? 10. An apparatus for application management, comprising: The first determining module is configured to determine, during the operation of a cloud application, the performance information of the service device used to run the cloud application, wherein the performance information indicates the usage status of the computing resources of the service device; The second determining module is configured to determine the target data processing volume of the text recognition module associated with the cloud application based on the performance information. A providing module is configured to provide multiple image frames of the cloud application to the text recognition module according to the target data processing volume, so as to determine the text recognition results of the multiple image frames, wherein the text recognition results reflect whether the text content included in the multiple image frames meets preset constraints; as well as The management module is configured to manage the operation of the cloud application based on the text recognition results.

11. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which, when executed by the at least one processing unit, cause the electronic device to perform the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of claims 1 to 9.

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